AI Surveillance Tracking for Subject-Object Interaction Detection
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Solution Overview
Problem
Existing surveillance systems struggle to efficiently monitor large volumes of video data for suspicious events involving interactions between subjects and objects, requiring manual intervention and significant resource allocation, and deep neural networks require extensive training data for effective detection.
Innovation Solution
An AI-based system that automatically tracks and monitors subjects and objects by extracting features, detecting interactions, and tracking subject-object pairs using neural networks, reducing the need for manual intervention and minimizing resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep neural network-based detectors are used to detect events, then detection accuracy is improved, but a large quantity of training data is required making the application impractical
Solution Approach 1:
The patent employs a single deep neural network detector that can detect multiple types of events (person-object interactions, object-object interactions, suspicious behaviors) without requiring separate detectors for each event type. This universal detector is trained on diverse interaction data and can identify various suspicious events through a unified model, significantly reducing the training data requirement compared to training separate detectors for each event type while maintaining high detection accuracy across different event categories
2Measurement precision
If manual operation is used to find or identify suspicious events, then detection precision can be controlled, but response time is delayed and productivity is reduced
Solution Approach 1:
The system automatically performs the entire surveillance analysis process without requiring manual intervention. The deep neural network detector autonomously analyzes video data, identifies suspicious events, and triggers alerts immediately upon detection. This self-service capability eliminates the time-consuming manual review process while maintaining detection precision through the sophisticated AI model, thereby significantly improving response time and productivity
3Area of stationary object
If object association is leveraged to find target objects, then monitoring coverage is improved, but manual operation is still needed to identify suspicious events
Solution Approach 1:
The patent implements fully automated surveillance analysis where the deep neural network detector autonomously identifies suspicious events based on object associations and interactions. The system automatically analyzes spatial relationships between persons and objects, detects abnormal patterns, and triggers alerts without requiring manual intervention. This complete automation maintains comprehensive monitoring coverage while eliminating the need for manual operation in event identification
Data Source
AI summary
A method for tracking and monitoring subjects and a plurality of objects. The method may include obtaining an image, wherein the image contains the subjects and the plurality of objects; extracting the subjects and the plurality of objects in the image through first feature extraction; detecting object interactions between the subjects and the plurality of objects; and tracking, through second feature extraction, subject-object pairs having detected object interactions.