3D Event Capture and Image Transform for Surveillance
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Solution Overview
Problem
Conventional video surveillance systems face challenges in real-time event detection and alerting due to data congestion and viewer inattention, as they rely heavily on human observation of multiple camera feeds, which leads to inefficiencies in monitoring and forensic analysis.
Innovation Solution
A 3D video analysis and alerting system that uses skeletonization circuits in security cameras to detect and transmit only relevant events, such as falling or threatening postures, by identifying pixel blocks corresponding to extremities and transforming images to focus attention on critical areas, thereby reducing network congestion and enhancing alertness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all video frames from multiple cameras are transmitted and stored, then complete surveillance coverage is achieved, but network bandwidth is consumed and data congestion occurs
Solution Approach 1:
The system extracts only the essential information from video frames by performing skeletonization to identify human figures and their poses, then transmits only these extracted features rather than complete video frames. This selective extraction reduces bandwidth consumption while maintaining surveillance effectiveness.
Solution Approach 2:
The video data is segmented into essential components (skeleton information, pose data) and non-essential components (full frame details). Only the segmented essential portions are transmitted, allowing complete surveillance coverage with reduced bandwidth usage.
2Reliability
If multiple camera feeds are monitored simultaneously, then comprehensive surveillance is achieved, but viewer alertness deteriorates due to monotony
Solution Approach 1:
The system extracts only meaningful events and anomalies from multiple camera feeds, presenting these extracted events to viewers rather than requiring continuous monitoring of all feeds. This maintains comprehensive surveillance while improving viewer alertness by eliminating monotony.
Solution Approach 2:
Instead of continuous monitoring of all camera feeds, the system periodically analyzes frames for events and anomalies, then presents only these periodic findings to viewers. This periodic event-based approach maintains surveillance effectiveness while reducing viewer fatigue.
3Measurement precision
If skeletonization and event detection are performed on all video frames, then event detection accuracy is improved, but processing time increases
Solution Approach 1:
The system performs preliminary skeletonization and pose estimation on video frames to identify potential events before detailed analysis. This preliminary action filters out non-event frames early, allowing accurate event detection only when necessary, thus reducing overall processing time while maintaining detection accuracy.
Solution Approach 2:
The system applies full skeletonization and event detection processing only to frames that contain relevant information (frames with detected skeletons or potential events), rather than processing all frames equally. This partial application of processing maintains accuracy for critical frames while reducing total processing time.
Data Source
AI summary
Images are selectively captured and transmitted by 3D security cameras to avoid congestion of a network coupling them to a central server. Skeleton detection circuits enable conditional event capture when triggered. Head, hands, and feet are associated with pixel blocks. An artificial horizon is derived from a shoulder segment. An isometric floor perspective is derived from a series of foot positions. Orientation of feet, head, or hands relative to an artificial horizon triggers event capture and transmission. Simultaneous position of two feet above the floor perspective triggers event capture and transmission. Images are transformed to effectively alert a user.