Video Surveillance Alert Generation via Selective Action Recognition
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Solution Overview
Problem
Current video surveillance systems face challenges in efficiently generating alerts for prohibited actions in real-time due to high computational loads and the need to process vast amounts of information from multiple cameras, making it impractical for operators to monitor and react promptly to suspicious activities.
Innovation Solution
The method involves identifying and tracking specific individuals within a subset of the image frame using appearance models and feature vectors, reducing computational load by performing action recognition only on the targeted individual, and generating alerts when prohibited actions are detected, with the option to transfer processing to a server for more complex verification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If action recognition is performed on all video streams from multiple cameras, then comprehensive surveillance coverage is achieved, but computational load becomes excessive and real-time processing becomes impractical
Solution Approach 1:
The video stream is segmented into multiple image frames, and action recognition is performed selectively on specific frames rather than processing the entire continuous video stream. This segmentation allows the system to maintain surveillance coverage while reducing the total computational burden by analyzing only discrete temporal segments.
Solution Approach 2:
The system extracts only the necessary image frames from the video stream that contain potential actions of interest, rather than processing all frames. This extraction approach isolates the critical information needed for action recognition while discarding redundant data, thereby reducing computational load while maintaining effective surveillance.
2Reliability
If comprehensive action recognition is performed on all video data, then all prohibited actions are detected, but processing time increases and real-time alert generation becomes difficult
Solution Approach 1:
The system performs preliminary processing by identifying and selecting specific image frames that are most likely to contain actions of interest before performing the computationally intensive action recognition. This preliminary selection step prepares the data in advance, allowing faster processing when actual detection is needed and enabling real-time alert generation.
Solution Approach 2:
Instead of performing exhaustive action recognition on all possible video data, the system applies partial action recognition only to selected image frames that meet certain criteria. This partial approach achieves sufficient detection accuracy for prohibited actions while dramatically reducing processing time to enable real-time operation.
3Loss of information
If operators review all live video streams manually, then complete situational awareness is achieved, but operator workload becomes unmanageable and response time increases
Solution Approach 1:
The system introduces an intermediary automated action recognition component that processes video frames and identifies prohibited actions before presenting information to operators. This intermediary handles the computationally intensive analysis, filtering and preparing information for operators, thereby maintaining complete situational awareness while dramatically reducing operator workload and improving response time.
Data Source
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AI summary
A method for generating an alert signal in a surveillance system is disclosed. The method comprises detecting a targeted individual in a video stream, selecting the targeted individual, and tracking the targeted individual, as first steps. Following these steps the method comprises classifying actions of the detected individual over a plurality of image frames in the video stream in response to the identification of the detected object as being a targeted person, and generating an alert signal if the classified action of the object is classified as a predefined alert-generating action.