Edge Camera Event Detection Using Object-Driven Frame Selection
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Solution Overview
Problem
Logistics in environments with multiple mobile and stationary objects are challenging due to the difficulty in coordinating their movements to prevent undesirable interactions, requiring improved autonomy and accurate object positioning and event detection.
Innovation Solution
Implementing sensor-equipped nodes with detection models trained on video and sensor data to identify video frames of interest, using event and detection models to infer events, and a decision pipeline for proactive actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If detection models are deployed at edge nodes for real-time event detection, then decision-making speed and autonomy are improved, but computational resources and storage capacity are limited
Solution Approach 1:
The system segments the detection task by deploying multiple specialized detection models at different edge nodes rather than using a single centralized system. Each model is trained on specific video data from particular cameras, dividing the computational workload across multiple nodes with limited resources each, enabling real-time detection without overwhelming individual edge devices.
Solution Approach 2:
The system processes only video frames that contain objects of interest rather than analyzing all video data. The object model identifies frames with objects, and only these selected frames are processed by detection models, reducing computational requirements while maintaining detection effectiveness for safety-critical events.
2Measurement precision
If all video data is processed and stored for event detection, then detection accuracy is improved, but storage costs and processing time increase
Solution Approach 1:
The system extracts only the necessary video frames containing objects of interest from the complete video data stream. The object model filters video frames to identify those with relevant objects, and only these extracted frames are stored and processed by detection models, significantly reducing storage requirements while preserving detection accuracy for safety events.
Solution Approach 2:
Different processing quality levels are applied to different video frames based on their content. Frames containing objects of interest receive full processing and storage, while frames without objects are discarded. This local quality differentiation ensures high detection accuracy for critical frames while minimizing overall storage requirements.
3Adaptability or versatility
If multiple detection models are deployed for comprehensive event detection, then detection coverage is improved, but system complexity and training requirements increase
Solution Approach 1:
The detection system is segmented into specialized models, each trained on video data from specific cameras and focused on detecting particular event types. This segmentation allows comprehensive detection coverage across the warehouse while keeping individual model complexity manageable and training requirements distributed across multiple smaller datasets.
Solution Approach 2:
The detection models are designed with multi-functionality to detect various event types (collisions, falls, unauthorized access, etc.) using the same underlying architecture and training approach. This universality reduces system complexity by using a consistent framework across multiple detection tasks rather than requiring separate specialized systems for each event type.
Data Source
AI summary
Object driven event detection is disclosed for nodes in an environment. Video frames of interest are identified from the video streams of cameras in the environment. The video frames of interest are input, along with node positions for nodes in the area of coverage of the cameras, into a detection module. The output of the detection model, combined with the output of an event model, are used by a decision pipeline to make decisions and perform actions in the environment.