Camera Topology Prediction for Faster CCTV Target Reappearance Tracking
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Solution Overview
Problem
Existing real-time and remote monitoring systems using video surveillance cameras face inefficiencies in target tracking due to large camera networks, camera malfunctions, and uncovered areas, leading to lengthy searches and uncertainty about target reappearances.
Innovation Solution
A real-time surveillance system utilizing an artificial predictive neural network that learns camera topology and predicts the next likely camera for target appearance by correlating target signatures across multiple cameras, enabling incremental learning and improved accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a search is performed across all CCTV cameras to identify a target, then the target can be located, but the search becomes lengthy in the case of a large number of cameras
Solution Approach 1:
The system performs preliminary actions by pre-learning camera topology and target movement patterns through incremental learning from historical data. The predictive neural network is trained in advance to anticipate where targets are likely to appear next, so when a search is needed, the system already has pre-computed probability rankings of cameras rather than needing to search all cameras equally.
Solution Approach 2:
The patent replaces the mechanical brute-force search approach (checking each camera sequentially or in parallel) with an intelligent predictive system using neural networks. The predictive neural network substitutes for the manual or systematic search process, using learned patterns to directly predict which cameras are most likely to contain the target, thereby reducing search time while maintaining identification accuracy.
2Adaptability or versatility
If the target moves into an uncovered area, then the target may escape detection, but it becomes difficult to know which camera the target might reappear on
Solution Approach 1:
The system uses feedback mechanisms where the predictive neural network continuously learns from actual target appearances and movements. When targets move through uncovered areas and reappear on cameras, this information feeds back into the incremental learning process, allowing the system to refine its understanding of camera topology and movement patterns, improving future predictions for uncovered areas.
Solution Approach 2:
The system performs preliminary mapping of camera topology and potential target paths before targets actually move into uncovered areas. By pre-learning the spatial relationships and typical movement patterns from historical data, the system can predict which cameras might capture targets emerging from uncovered areas, rather than waiting for actual detections to infer topology.
3Reliability
If a camera may malfunction or cover is incomplete, then surveillance reliability decreases, but the system should still be able to predict target reappearance
Solution Approach 1:
The system handles camera malfunctions and incomplete coverage by dynamically adjusting prediction parameters rather than requiring perfect camera coverage. The predictive neural network can adapt its predictions based on the operational status of cameras and the completeness of coverage, modifying its output probabilities to account for missing or malfunctioning cameras while maintaining reliable target reappearance predictions.
Data Source
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AI summary
The invention relates to a surveillance system for at least one site comprising video surveillance cameras, the surveillance system comprising at least one surveillance camera management module and an artificial neural network for predicting the location of a target in an area monitored by a camera, connected to the surveillance camera management module, the artificial neural network for prediction comprising an input for acquiring target information comprising data for prediction comprising data: - identification of the camera in which a target has been detected, - positioning of the target at the output state, the artificial neural network for prediction comprising an output of at least one identification of a probable camera whose target will probably be identified and the camera management module comprising an output for transmitting the at least one probable camera identification to a device comprising a screen.