In the context of the comprehensive digitalization of police work, the public security view library has become a key support platform for modern policing practice. It brings together massive video surveillance data and provides strong data support for core businesses such as criminal investigation tracking, traffic governance, and public security prevention and control. However, different police types (such as criminal police, traffic police, and public security police) face their own unique task goals and work scenarios, and the requirements for view libraries are also significantly different. This article will discuss in depth how to realize the in-depth customized application of the public security view library according to the business characteristics of different police types, so as to improve the actual combat effectiveness of policing.
In the context of the comprehensive digitalization of police work, the public security view library has evolved from a simple video storage system to a key platform to support modern policing practice. It brings together massive data generated by urban intensive surveillance probes, portrait bayonets, and vehicle identification points, providing strong data support for core businesses such as criminal investigation and tracking, traffic governance, and public security prevention and control.
However, one reality is that criminal police, traffic police, and public security police face different task objectives, work scenarios, and core needs. Expecting a “universal version” view library to meet the needs of all police types is unrealistic and limits the deep release of data value. How to transform the view library from a common platform to a customized tool that deeply fits the workflows and core needs of each police type?
This is not only a technical problem, but also the key to improving the effectiveness of police operations.
This article will deeply analyze the business characteristics of different types of police and discuss the strategies and practices of in-depth customization of view databases.
1. Understand the business characteristics of police types: the root cause of demand differences
To make technology truly serve actual combat, we must first understand the core logic and pain points of different police branches.
Criminal investigation department: clue integration and trajectory restoration
Core requirements:
- Lock the target person/vehicle, accurately restore the criminal trajectory, and form an impeccable chain of evidence.
- The requirements for data accuracy, completeness, and spatiotemporal continuity are almost demanding.
- A blurry license plate or a missing video can break the clue.
Practical Scenarios and Technical Challenges:
Imagine facing a well-planned cross-district theft case. The suspect deliberately avoided the main surveillance and frequently changed his means of transportation and clothing.
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The problem for criminal police is: how to find out the “ghost” in a huge video stream that spans several days, covers multiple blocks, and involves hundreds of probes?
They often can’t afford a clear frontal photo, but need to capture gait characteristics, habitual small movements, gradients in clothing details, and uniqueness of their belongings. More importantly, it is necessary to intelligently connect these seemingly isolated fragments and piece together a complete chain of behavior of “stepping on the point, implementing, escaping, and selling stolen goods”.
This places extremely high demands on the view library:
- a powerful spatiotemporal correlation analysis engine that can automatically correlate clues across time and space;
- Advanced video intelligent enhancement technologies (such as super-resolution reconstruction, deblurring, low-light enhancement, and deshaking) can bring to the surface the key details in the dim, shaky, and occluded images that were originally “unclear”.
A system that can understand “cross-temporal and spatial correlations” and “feature continuity” is the rigid need of criminal investigation.
Traffic Police: Traffic Management and Law Enforcement
Core requirements:
- Grasp the pulse of road conditions in real time, quickly investigate and deal with traffic violations, and accurately restore the accident scene.
- It is extremely dependent on the real-time nature of data, recognition accuracy and system response speed.
- A delay of a few seconds can be the beginning of congestion;
- The misidentification of a license plate may lead to deviations in law enforcement.
Practical Scenarios and Technical Challenges:
In the morning and evening rush hour, the urban aorta is full of traffic. On the large screen of the command center, the view library must collect and process the traffic flow data of each intersection bayonet in real time.
When a key intersection suddenly collided, it instantly triggered a chain reaction.
The traffic police need to retrieve high-definition real-time video from multiple angles before and after the accident point in seconds to clearly judge the moment of collision and the division of responsibilities; At the same time, the view library needs to quickly analyze the real-time traffic data of the surrounding road network, and drive the intelligent traffic signal system (such as SCATS/SCOOT) to optimize remote timing and divert.
Investigate and deal with a “deck car” that wandered around the city and committed crimes? This requires the view library to seamlessly integrate bayonet data from different administrative districts and even different construction standards, build a unified vehicle traffic file, and realize the second-level traceback of cross-regional vehicle trajectories and the automatic association of illegal records.
Any data silos, format incompatibles, or errors in the recognition engine can be a stumbling block to efficiency. High concurrency real-time processing capabilities, accurate AI recognition algorithms (license plates, models, body features), and cross-domain data fusion mechanisms are the cornerstones of traffic police business.
Public security police: situational awareness and risk prevention and control
Core requirements:
- Real-time perception of the overall social situation, intelligent early warning of potential risks, and rapid linkage to deal with emergencies.
- The coverage of data (key areas), the accuracy of intelligent analysis and early warning, and the intuitive visualization of situation information are extremely high.
- “See everything”, “understand” and “react quickly” are the keys.
Practical Scenarios and Technical Challenges:
A large-scale concert with tens of thousands of people is about to end. The public security command center monitors the dynamics of each entrance, exit, passage and square in real time through the view library platform.
The system needs to calculate and visualize the heat map of people flow and crowd density in real time based on intelligent algorithms, and continuously analyze the flow rate, flow direction, and behavior patterns of the crowd (such as normal travel, gathering, pushing, running, falling, abnormal retention, and item retention).
Suddenly, the system detects a characteristic pattern of a sharp increase in density, a sudden drop in flow rate, and local pushing behavior in a narrow outlet area, and immediately triggers a graded warning (e.g., orange). The commander needs to instantly retrieve the multi-angle high-definition real-time picture and associated video clips of the area for manual review and confirmation, and immediately command the nearby mobile police force through the intercom system, the on-site security personnel to intervene and guide them, and even the linkage broadcast system to guide them, so as to resolve the potential stampede risk in the bud.
In daily work, intelligent identification and early warning of abnormal behaviors (such as long-term wandering, tailgating, and leaving belongings) in key places (stations, business districts, hospitals) are the “golden eyes” of public security prevention and control.
The challenge is:
- How to define “abnormal” in the complex and changeable behavior of the crowd?
- How does the algorithmic model adapt to different scenarios (stadium exit vs open square)?
- How to ensure recognition accuracy in high-density environments and reduce false positives (misjudging fights as brawls) and false positives (covert pickpocketing preparations)?
This requires highly scenario-based AI model training and continuous optimization.
2. Challenges in the application of view library: commonality and individuality
The view library is of great value, but in actual implementation, all police types face common challenges and their own pain points.
Common challenges: the basic bottlenecks of technology application
Low retrieval efficiency:The volume of data has exploded, and the traditional method of dragging and searching based on timelines and simple keywords (such as “red shirt”) is extremely inefficient. In order to find the key seconds of the suspect, the police may need to constantly sift through hours or even days of footage, which is extremely fatiguing and easy to miss key information. The lack of efficient and intelligent cross-camera and cross-time and spatial retrieval capabilities is a common pain point.
Poor original video quality:Factors such as old equipment, complex lighting conditions (strong backlight, night), and bad weather (rain, fog, snow) lead to a large number of poor video quality. Blurred faces, unrecognizable license plates, and no matter how advanced AI algorithms are, they can’t do anything. The quality of the original video is uneven, which seriously affects the subsequent intelligent analysis and evidence validity.
Data sharing is difficult:Problems such as departmental barriers, geographical restrictions, system heterogeneity (different manufacturers, different periods of construction), and inconsistent data standards still exist. The traffic police cross-city chase clues are interrupted, and the criminal investigation cross-provincial collusion case investigation process is cumbersome and time-consuming, which is rooted in the fact that the data cannot “flow” smoothly. The lack of unified data access, governance, sharing standards and efficient platforms has led to a significant reduction in comprehensive efficiency.
Personality pain points: the core bottleneck of the police business
Criminal investigation:The core challenge of serial cases and wandering crimes lies in how to intelligently correlate fragmented clues (blurred portraits, suspicious vehicle clips, specific behaviors) scattered at different times, in different places, and even in different cases. The clue association function of existing view libraries is often relatively basic, mainly relying on manual time-consuming spatio-temporal comparison and feature matching, which is inefficient and easy to miss key connection points. Lack of powerful cross-case, cross-temporal and space-time clue automatic mining and correlation reasoning engine.
Police:The mobility of vehicles naturally requires the interconnection of view data, but the reality is often “drawing the ground as a prison”. The lag in obtaining information on illegal vehicles in different places (relying on manual co-inspection), the difficulty of restoring cross-regional accident sites (lack of perspective), and the inefficiency of precise crackdowns on deck vehicles (inability to quickly compare global traffic records). The core pain point lies in the lack of efficient, standardized and automated cross-regional vehicle view data sharing and exchange mechanism and practical platform.
Peace:The complexity of crowd behavior and the diversity of scenarios make it difficult for existing intelligent analysis algorithms to define and identify abnormal behaviors. A high false positive rate can lead to the “wolf is coming” effect, which consumes valuable police resources. False negative, on the other hand, can mean missed opportunities for disposal and serious consequences. How to make the algorithm model more deeply understand the nuances of “normal” and “abnormal” in different public security scenarios? How to establish a more scientific and scenario-based multi-level early warning threshold model? This is a bottleneck that needs to be broken through in the application of public security.
3. In-depth customization strategy: build exclusive functions for police types
To solve the above problems, it is necessary to deeply customize the core business flow of the police type, and optimize data governance, processing processes, intelligent algorithms, and human-computer interaction.
Data-centric: Scenario-oriented governance and integration
Criminal investigation: building a “case clue data center”
Data Scraping Strategies:After the case occurs, priority and automatic capture of high-definition video sources within the time and space range of the case (N hours before and after the incident, radiating X kilometers around), especially at key nodes (entrances and exits, necessary roads, and easy hiding points). Establish a pool of video resources involved in the case.
Data Fusion Breadth:Strongly integrate resources, not only to build street monitoring, public security bayonets, and electronic police built by the public security, but also to establish an efficient and safe mechanism, access key social video resources (entrances and exits of the community, shop entrances, parking lots, and public areas inside the unit) on demand, and weave a “net of heaven and earth”. Explore the establishment of a quality evaluation and optimization mechanism for video access to social resources.
Data Preprocessing Enhancements:Automatically pre-enhance videos included in the case view resource pool (such as dejitring, low-light enhancement, and blurring) to significantly improve the usability of subsequent manual viewing and intelligent analysis. Establish a case-specific view dataset to facilitate subsequent in-depth clue mining and correlation analysis.
Metadata Enhancement:Richer structured annotations (time, location, camera ID, preliminarily identified object types/characteristics, etc.) are provided for video clips and captured images, making it easier to retrieve and associate efficiently.
Traffic police: build a “vehicle holographic archive”
Global Data Integration:Integrate multi-source vehicle traffic data covering all roads, including fixed bayonets, electronic police, mobile police terminals (on-board, handheld) capture, and even some public security monitoring with vehicle recognition capabilities.
Data Structuring and Indexing:Data is deeply structured at the access layer or in storage: it is stored finely according to accurate timestamps, high-precision geographical location (GPS coordinates or road section stations), license plate number (OCR results and confidence), vehicle make/model/model, body color, and distinctive features (sunroof, stickers, damage, etc.). Establish multi-level composite indexes (time + place, license plate + time, car model + color + time, etc.) to ensure millisecond-level retrieval response.
Real-time guarantee:The architecture design prioritizes low-latency access and processing pipelines for real-time data. Technologies such as message queues (such as Kafka) and streaming processing engines (such as Flink) are used to ensure that the passing data can be stored and queried within seconds.
Cross-domain collaboration mechanism:Promote the establishment of provincial/municipal and even regional level vehicle view data exchange standards and sharing platforms. It adopts unified data formats (such as GA/T 1400 view library standards), secure data transmission protocols, and standardized interfaces (APIs) to truly open up the “two veins” of vehicle data, and support cross-regional trajectory traceability and collaborative investigation and punishment of violations.
Public security: Build a “situational awareness network in key areas”
Data Access Focus:For video streams in highly densely populated places (transportation hubs, business districts, scenic spots, and large-scale event venues), areas with complex public security, and key security targets, key monitoring, high-priority bandwidth guarantees, and redundant storage are implemented.
Scenario-based preprocessing:When data is accessed, real-time or near-real-time preprocessing is carried out in combination with the requirements of public security services:
- Calculate people flow statistics (entry/exit/stay) in real time using background modeling, object detection and tracking algorithms.
- Generate high-precision density heat maps in real time based on crowd distribution.
- Baseline modeling of behavior patterns (learning the flow rate and distribution of people under normal conditions) in specific areas of concern (such as security checkpoints, narrow aisles, and storage areas) lays the foundation for anomaly detection.
Data Labeling:The video data is labeled with rich scene semantic labels (such as “entrance to the west square of the railway station”, “exit of the grandstand in Area A of the concert”, and “emergency hall of the hospital”) to facilitate subsequent targeted analysis and rapid scenario-based retrieval and rule application.
Functional advancement: Intelligent empowerment that is close to actual combat
Criminal investigation: clue mining and evidence strengthening
Smart Lead String Engine:Develop core functions based on graph computing technology. The engine is capable of:
- According to the spatio-temporal proximity (coincidence or continuous movement of the time and place of the crime), the similarity of object characteristics (people: gait, body shape, clothing; Car: model, color, features; objects: backpacks, handbags), behavior pattern matching (wandering, snooping, fast approaching/leaving) and other multi-dimensional features.
- Automatically discover and associate clue fragments that may belong to the same target or chain in different cases and monitoring points.
- Intelligently speculate on possible correlations between leads and calculate confidence levels.
- The results are visualized as a dynamic clue map (knowledge graph) or spatio-temporal trajectory map, which clearly displays the “activity puzzle” of the suspect/vehicle to assist investigators in quickly forming investigation hypotheses.
Video Enhancement & Feature Enhancement Toolkit:Integrating industry-leading CV algorithms to provide one-stop video processing capabilities:
- Super Resolution Reconstruction:Improve the clarity of low-resolution video (e.g., from 720P to 1080P perception).
- Video unblur:Effectively improve motion blur and out-of-focus blur.
- Low-Illumination Enhancement:Significantly improve the visibility of videos at night or in dimly lit environments (e.g., Retinex, Zero-DCE, etc.).
- Video deshaking:Stabilizes screen shake caused by wind or unstable installation.
Local feature labeling and comparison:Provide special tools to allow investigators to highlight and enlarge parts of the human body (scars, tattoos, birthmarks), face parts (moles, glasses features), and vehicle parts (unique ornaments, car stickers, damage, modifications) in the enhanced picture, and support similar feature comparison across videos.
Traffic police: efficiency and precise law enforcement
One-click traceback of vehicle trajectory:Enter the license plate number (or blur the combination features such as license plate + model color), and the system will generate a complete driving trajectory animation of the car in the whole city (expandable to neighboring cities and provinces through the cross-domain platform) in a selected time period (such as the past 24 hours, a week) in seconds based on the powerful spatio-temporal index and GIS engine. Accurately mark the time, speed, and direction of the vehicle passing through each associated bayonet/monitoring point. It supports overlaying real-time traffic conditions layers (red, yellow, and green), historical traffic flow data, and signal light status to assist in understanding driving path selection.
Intelligent snapshot of illegal facts:Combined with the real-time/historical trajectory data of the vehicle and the built-in rule library of illegal locations (such as prohibiting left turns at an intersection, speed limit value in a certain section, solid line area), the video is used for intelligent analysis:
- Automatically identify and intercept key video clips (5-10 seconds) and high-definition pictures (at least 3 photos, including panoramas, close-ups of license plates, and illegal moments) of violations (such as running red lights, compacting lanes, driving without following the guide lane, illegal parking, etc.).
- Automatically associate basic vehicle information, owner information, and historical violation records.
- Automatically generate a complete chain package of illegal evidence including time, place, type of violation, and link to evidence pictures/videos, greatly simplifying the process of police evidence collection and entry, and realizing “what you see is what you punish”.
Public security: situational awareness and risk warning
Intelligent perception and early warning platform of crowd situation:Deeply integrate multiple algorithms to build all-round perception capabilities:
- High-precision people counting:Based on deep learning object detection and tracking (such as YOLO, DeepSORT), accurate counting of entrances, exits, and area boundaries is realized.
- Real-time density heat map generation:Based on crowd distribution points, it quickly generates a visual heat map to visualize the congested area.
- Group behavior pattern recognition:The specialized model is trained to identify key situations such as Crowd Gathering, Crowd Dispersion, Crowd Pushing, Crowd Running, Person Falling, Loitering, and Abandoned Object.
- Multi-level early warning threshold model:Set differentiated early warning rules and thresholds (e.g., density threshold, flow rate threshold per area, and specific behavior duration threshold) based on the physical characteristics of different scenarios (e.g., stadium exit narrowness, plaza openness, channel length) and activity nature (daily vs. large-scale events). Introduce a confidence mechanism to reduce false alarms caused by changes in light and shadow and behaviors of special groups (such as star chasing). Early warning information is pushed by pop-up windows linked to electronic maps and associated real-time video images.
Key personnel/item deployment and control linkage:Deeply connected with the public security “big intelligence” system, fugitive persons database, lost persons database, and deployment and control object database. In the video stream accessed by the view library, face recognition, human Re-ID (re-identification), and item recognition technologies are used to automatically detect specific targets (such as fugitives across the country, lost elderly and children, suspicious packages/vehicles), continuous cross-camera tracking, and real-time alarm. Alarm information is pushed to the mobile police terminal or command platform of the relevant responsible police.
Interactive Efficiency: Interface (UI/UX) in line with the mindset of the Alert
Criminal investigation interface: centered on “case/clue”
Core Layout:The main interface adopts “three-view linkage”: timeline (clearly marks the key time nodes and development context of the case) + high-precision electronic map (spatial distribution, intuitively displaying clue points and trajectories) + clue list/atlas (structured display of clue entities such as people, vehicles, objects, and behaviors and their correlations).
Key features:
- Collaborative annotation tools:Support multiple investigators to circle, annotate, tag, and establish clue associations on the same video at the same time online (e.g., “Figure X in Video A” is associated with “Suspect Y in Case B”). Annotation information is shared synchronously in real time.
- Smart Screening:It supports rapid filtering of massive videos by multiple features (time range, location range, object type, color, behavior label) and combination conditions to accurately locate target clips.
- Visualization of the Relationship Graph of Clues:Dynamically display the correlation network between clues, and support drilling to view details. The operation logic needs to closely fit the investigator’s thinking habits of “point-to-surface, correlation divergence, and hypothesis verification” to reduce the operation level.
Traffic police interface: with “map + vehicle” as the core
Core Layout:The electronic map occupies the absolute main visual. Real-time superimposed rich traffic information layers: traffic flow (color-coded for smooth/slow/congested), real-time congestion index, accident/event points (icon + details), heat map of areas with high incidence of violations, real-time status of intersection signals (red/green/yellow).
Key features:
Vehicle Enquiry Portal:Enter the license plate number to animate its historical trajectory on the map (can be fast-forward, slow-down, pause), or predict its current location/possible path based on the latest bayonet data.
Integration of Illegal Handling Modules:The evidence package generated by “vehicle trajectory trace” and “intelligent snapshot of illegal facts”, the illegal information entry interface, and the generation of simple penalty decisions are integrated into a smooth process to achieve “one-stop” processing. The interface design pursues a high degree of information aggregation, key indicators at a glance (such as the current congestion index, number of accidents), and one-click direct access to core operations (trajectory query, illegal entry).
Public security interface: emphasizing “situational monitoring and rapid response”
Core Layout:The multi-screen real-time monitoring wall is the foundation, which supports flexible segmentation (1/4/9/16, etc.) and quickly switches between key area screens. The core situation information is displayed on the monitoring screen or sidebar: real-time heat map, people flow statistics in key areas, and real-time early warning information pop-up windows (graded color prompts).
Key features:
- Quick emergency operation:Provide eye-catching “one-click call plan” (display the preset disposal process), “one-click notification of nearby police forces” (distribute instructions through the police communication or intercom system), and “one-click playback associated recording” (quickly view the screen of N minutes before the early warning is triggered) buttons.
- Optimized design of large screen:The interface UI is specially optimized for the large-screen display of the command center: the font is large enough, the key information (early warning level, pedestrian flow, heat map) has strong color contrast, and the button size is moderate and easy to touch. The core goal is to ensure that commanders can see the situation at a glance and complete key operations in one step in an emergency.
4. Practical effect: the efficiency leap brought about by customization
The value of technical solutions must ultimately be tested on the real police battlefield.
Case 1: Criminal investigation quickly cracked the cross-regional speeding car robbery case
Case:In a city, there have been continuous cross-regional speeding car robbery cases, and the suspects have a strong sense of anti-investigation, frequently changing motorcycles, helmets and clothes, and the traditional platoon has reached a stalemate.
Customized Technology Applications:Criminal Investigation enables the newly deployed Customized View Library platform.
- The investigators entered several vague suspect side video clips (different times and locations) and local characteristics of the motorcycle (such as the unique rear shelf binding method and exhaust pipe shape) into the “intelligent clue string and engine”.
- Based on spatio-temporal analysis, human Re-ID feature comparison, and vehicle local feature matching, the engine automatically correlates dozens of seemingly unrelated monitoring point video clips scattered across three administrative districts and spanning a week within a few hours.
- Results: The system automatically generated a complete closed-loop trajectory map of the suspect, clearly showing the locations where he changed motorcycles in different areas, the buildings in the urban villages where the hideouts were located, and the second-hand mobile phone market stalls where the stolen goods were sold. The trajectory map is dynamically presented on the electronic map.
- Key breakthrough: Using the super-resolution reconstruction and deblurring algorithm in the “Video Enhancement Toolkit”, an extremely blurry video of the entrance of an urban village at night was processed, and the relatively clear facial features of the suspect at the moment of taking off the helmet were successfully extracted, providing key support for identity locking.
Effect:Based on the accurate clue map and enhanced images output by the system, the task force quickly locked the identity of the suspect and the hiding place, successfully arrested the suspect within 72 hours after the incident, and eliminated a wandering crime gang. The efficiency improvement of clue association and key feature extraction is the core accelerator of solving crimes.
Case 2: The traffic police efficiently handle traffic violation reports
Pain point:In the past, the traffic police of a city handled traffic violations reported by the masses (such as “not driving in the guide lane”), and the police needed to manually find the monitoring points near the reporting location, and repeatedly play back the video to find the target vehicle and the moment of violation, which took an average of more than 30 minutes.
Customized Technology Applications:The detachment has focused on deploying the functions of “one-click backtracking of vehicle trajectory” and “intelligent snapshot of illegal facts” in the customized view library.
- After receiving the report, the police only need to enter the license plate number of the reported vehicle.
- Based on the cross-bayonet data, the system generates the animation trajectory of the vehicle’s driving route during the reporting period in seconds, and automatically locates the exact intersection and time when the violation occurred.
- The intelligent snapshot function automatically captures 3 pictures from different angles and a 5-second key video containing clear license plates and complete violations (clearly showing the vehicle changing lanes in the solid line area).
- The system automatically packages and generates a complete chain of evidence containing the above evidence, time, location, and type of violation.
Effect:The police only need to quickly review and confirm the evidence package automatically generated by the system to complete the illegal entry. The entire process has been shortened from an average of more than 30 minutes in the past to less than 5 minutes. The efficiency of police handling has been doubled, the feedback from the masses has been more timely, and the standardization of law enforcement has also been strengthened. Automated chain of evidence generation is key to efficiency leaps.
Case 3: Public security effectively prevents the risk of stampede by large-scale activities
Scenario:A super-large outdoor music festival, tens of thousands of spectators are about to end.
Customized Technology Applications:The security headquarters relies on the “crowd situation intelligent perception platform” of the public security customized view library throughout the process.
- On the large screen, the real-time updated heat maps and people flow statistics of each region are clear at a glance.
- 15 minutes before the end of the performance, based on the data of the camera installed in a narrow exit area, the system detected a sharp increase in instantaneous crowd density (exceeding the preset orange threshold) and a sharp drop in the average flow rate of the crowd, and the algorithm identified obvious pushing behavior patterns in the local area. The system immediately triggers an orange warning pop-up.
- The early warning information is accurately located to specific exits (such as “North District Exit 2”), and multiple high-definition real-time monitoring images in the area are automatically associated and pushed.
Effect:The commander instantly retrieved the screen to confirm the situation (the picture showed that the crowd formed an obvious obstruction and pushing at the exit), and immediately started the preset diversion plan: guide some people to divert to the backup exit through live broadcasting; Order to temporarily open spare exit gates; Through the intercom system, nearby mobile police and security personnel were mobilized to quickly strengthen the diversion force of the exit. It took only 3 minutes from the issuance of the system warning to the basics of the diversion measures. It effectively alleviated the pressure on the exit, avoided the risk of possible crowding and stampede, and ensured the safe and orderly departure of tens of thousands of spectators. Scenario-based multi-dimensional real-time perception and hierarchical early warning mechanism are the key to success.
5. Summary and future direction: towards intelligence and integration
The differentiated application and in-depth customization of the public security view library is a system project guided by the core business pain points of the police, driven by data intelligence, and aimed at actual combat efficiency. Practice has proved that successful customization can significantly shorten the crime solving cycle, improve law enforcement efficiency, and enhance security prevention and control capabilities. Looking forward to the future, the intelligent and personalized road of view library will develop in more depth:
1) AI-driven deeper insights:
- Predictive Policing:Combining computer vision (CV), machine learning (ML), graph neural network (GNN), as well as historical cases, social conditions, and spatio-temporal data, the view library will move from “post-retrospective” to “pre-prediction”. For example: predicting crime hotspots in specific areas and time periods; Evaluate the evolution trend and scope of traffic congestion; Identify more complex and hidden patterns of anomalous behavior (e.g., collaborative signals from pickpocketing gangs, suspicious stepping behavior in specific places).
- Cognitive Intelligence Upgrade:The AI model will not only “see” the picture, but also combine the context to “understand” the semantics of the scene (such as identifying the nuances of “quarrel” and “frolic”, “normal maintenance” and “destruction of public property”), and improve the accuracy of early warning and the depth of behavior understanding.
2) Multi-source data fusion applications:
- “View +” integration ecology:The view library will be carried out with the police system, big intelligence platform, PGIS (Police Geographic Information System), mobile police applications (such as the Police Communication APP), and even the Internet of Things perception network (such as WIFI probes, intelligent perception piles, and drone videos).Deep connectivity。 Break the “data chimney” and build a unified combat view that uses the view as the spatio-temporal benchmark and integrates multi-dimensional information such as people, vehicles, objects, electricity, and networks.
- Empowering front-line mobile terminals:The key capabilities of the view library (such as face/license plate/vehicle comparison, real-time video preview in key areas, and related information query) are safely and efficiently sunk into the mobile police terminal of the police, enabling on-site verification and rapid disposal.
3) Cloud-edge-end co-evolution:
- Cloud computing:It provides powerful computing power for massive storage, large-scale offline analysis (such as historical case consolidation, regular mining), and complex model training.
- Edge Computing:Edge computing nodes are deployed at bayonets and key areas close to data sources to realize front-end real-time intelligent analysis (such as real-time license plate recognition, face capture comparison, and local area crowd density calculation), greatly reducing transmission delay and bandwidth pressure, and improving response speed.
- End-side applications:Mobile police terminals as application touchpoints. The “cloud-edge-end” collaborative architecture will significantly improve the real-time, coverage, and intelligence level of view applications.
4) Continuous iterative customization:
- Agile response mechanism:Establish an efficient regular communication and feedback mechanism between police business experts and technicians (such as regular workshops and rapid prototyping), collect front-line feedback in a timely manner, and respond to new needs and challenges.
- DevOps vs. Continuous Delivery:Agile development and continuous integration/continuous deployment (CI/CD) models ensure that customized features can be iterated, tested, and launched quickly.
The construction and application of the public security view library has entered the stage of refinement. It is an essential component of modern policing combat effectiveness. Only by deeply understanding the unique business logic and working environment of different police types such as criminal investigation, traffic police, and public security, and through in-depth customization of data governance, intelligent algorithms, functions and human-computer interaction, can we give full play to the value of data and create special tools for police types that meet actual combat needs. This reflects the concepts of “science and technology to revitalize the police” and “data empowerment”, providing stronger, more accurate and efficient support for maintaining social stability and ensuring people’s safety, and promoting the development of smart policing. Customization is the key path to release the core combat effectiveness of view data, and it is also the inevitable direction of police modernization.
