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Background and challenges

The monitoring quantity is relatively small, the points are scattered, and the functions are single;
Low utilization of social resources;
The existing system lacks data mining, and accidents still rely mainly on manual viewing of videos;
Lack of application of human/vehicle technology and tactics, unable to adapt to current urban management needs.

Solution Introduction

Targets

Global Convergence: Promote the networking of public safety video monitoring systems, and integrate video and image resources from various key public areas, industries, and fields Full scale analysis: Provide intelligent full scale analysis, object association, behavior analysis, and structured information extraction of fully correlated target images centered on control objectives such as "people, vehicles, objects, and fields" Full police application: Utilizing video image analysis data to conduct business applications in departments such as public security, traffic police, criminal investigation, and intelligence, providing intelligent comprehensive applications for general, professional, and thematic views of the entire police force

Overview of the plan

Based on cloud computing architecture, structured video data is used as the foundation for clustering and archiving, providing richer and more effective practical applications, enhancing the ability to manage and control people/vehicles, and creating an upgraded transformation of provincial/municipal information construction from "passive investigation after the event" to "proactive warning before the event".

Logical architecture

Advantages of the plan

Ultra large scale:
Large scale data access and analysis: 5 million cameras connected, 300000 cameras distributed analysis and clustering (national level);
Large scale data aggregation and retrievals: Single node access and management of 10 billion structured data, single node support of 1 billion structured data (sliding window) second level response, single node 10 billion level data retrievals second level response;
Large scale image search: second level response;
Large scale archive management: 100 million level archives, 1 billion level snapshot details management;
High accuracy:
High accuracy facial algorithms: ZDR, FAR, 1:14E, major competitions, and various POC accuracy are far ahead; High accuracy clustering algorithm: with an accuracy rate of 99.9% under a billion level base database;
High accuracy ReID: The accuracy of POC testing is much higher than that of competitors;
High accuracy image search High accuracy facial recognition in complex scenes: wearing masks, severe occlusion, complex lighting, blurred images, cross age;
High practicality:
Big data professional applications: peer analysis, spatiotemporal collisions, one person with multiple vehicles (such as stealing three vehicles), frequent scene appearances;
Intelligent search professional applications: full-text search, image search, image search files, mixed image search, structured search, association search, comprehensive search, fusion search, etc;
Application of file features: facial body clustering, file merging, hierarchical clustering, clustering strategy, spatiotemporal clustering, file association analysis, image code fusion, binary fusion;
High usability:
Customized techniques and tactics: conditional touch and match classes, search and match classes, and library related collision classes;
Ultra high performance technology and warfare application: 100 million level data volume with second level response;
High availability deployment: large-scale distributed cluster deployment, cloud deployment, container deployment;
Localization deployment plan: Supports 5 heterogeneous computing solutions (of which 4 are localization solutions).

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Automaticfeature generation engine

Based on the first-mover advantage of Cloudwalk in the field of AI engineering, the automatic feature generation engine automatically generates the candidate features of the subject to be predicted by simply configuring the Meta information of the data and its aggregate statistics. This engine can generate corresponding experience based on the actual features summarized by algorithm engineers in a variety of scenarioses. Only by simply configuring the Meta information of tabular time series log data, a set of high-level baseline features can be obtained automatically.
Face recognition

The original face recognition algorithm developed by Cloudwalk possesses industry-leading advantages in face detection, face comparison, 1:N face recognition and face attributes.
Knowledgegraph

Cloudwalk possesses a complete set of knowledge graph products that cover multiple stages such as knowledge modeling, knowledge extraction, knowledge storage, knowledge computing, knowledge reasoning and knowledge application. Such knowledge graph products have many advantages, e.g. advanced concept, large capacity, excellent performance, abundant functions, convenient interaction and appropriate deployment.
Based on big data platforms and with graphic calculation as the core, our knowledge graph products help enterprise users in various fields to build their knowledge middle grounds, provide them with all-round knowledge assistance, and improve their level of intelligent operation and management.
Object detection

Adopting the deep learning model, the object detection algorithm developed by Cloudwalk leads in both algorithm and application:
• Algorithm advantage: Based on the advanced target detection algorithm of Cloudwalk and the most cutting-edge inference library, this object detection algorithm, which possesses a number of patents, remarkably optimizes the efficiency of feature extraction;
• Excellent performance: Fast recognition, high accuracy and real-time accurate feedback;
• High security: Adapting to the company’s soft and hard key authorization, the SDK layer encrypts the identification model to prevent the product from being copied;
• Strong adaptability: With great application value, this object detection algorithm is applied to scenarioses such as industrial production, power operation and engineering construction to efficiently perform functions like defect detection and helmet detection.