Video Stream Annotation via Multiple-Instance Learning
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Solution Overview
Problem
Current video surveillance systems require time-consuming and costly manual annotation for person re-identification, especially when dealing with videos from multiple cameras with high photometric and geometric variations, which complicates the training of machine learning models for accurate person detection and tracking.
Innovation Solution
A method and system for annotating video streams by scanning frames for persons, creating video tracks with regions of interest, and using multiple-instance learning to construct similarity models, reducing the need for extensive manual annotation and improving model training efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual frame-by-frame review is used to annotate video data, then annotation accuracy can be ensured, but the process becomes time-consuming and costly
Solution Approach 1:
The system enables automated self-annotation by training machine learning models to detect and track persons in video frames without requiring manual annotation of every frame. The model uses detected regions from previous frames to guide detection in subsequent frames, allowing the system to annotate itself efficiently while maintaining accuracy.
Solution Approach 2:
The system performs preliminary detection and tracking to identify candidate regions of interest before final annotation is required. By pre-processing video frames to locate potential person instances and tracking their trajectories, the system prepares annotated data in advance, reducing the time needed for final annotation while ensuring accuracy through pre-validated detections.
2Loss of information
If manual annotation of all frames is performed, then complete person tracking information is obtained, but the cost and complexity increase significantly
Solution Approach 1:
The annotation process is segmented into two stages: automated detection/tracking for most frames, and selective manual verification only when needed. The system divides the video stream into segments that can be processed automatically, with intelligence to identify and flag only ambiguous cases for manual review, thereby reducing overall complexity while preserving complete tracking information.
Solution Approach 2:
A machine learning model acts as an intermediary between raw video data and final annotations. The model processes video frames to generate detection results and track persons across frames, serving as a bridge that automatically extracts tracking information without requiring direct manual annotation of every frame, thus reducing system complexity while maintaining information completeness.
3Productivity
If automated detection is used without spatial tracking, then processing speed increases, but detection accuracy decreases due to photometric and geometric variations
Solution Approach 1:
The system maintains continuous tracking of detected persons across video frames by using spatial location information from previous frames to guide detection in current frames. This continuous action ensures that detection accuracy is maintained despite photometric and geometric variations, while still achieving high processing speed through targeted detection only in relevant regions rather than full-frame scanning.
Solution Approach 2:
The detection process applies local quality adaptation by adjusting detection parameters based on the specific spatial region being analyzed. Using location information from previous frames, the system tailors detection sensitivity and parameters to local conditions in each frame, improving accuracy for regions with persons while maintaining fast processing speeds by focusing computational resources only on relevant local areas rather than uniformly processing entire frames.
Data Source
AI summary
Systems and methods are disclosed herein for annotating video tracks obtained from video data streams. Video tracks are treated as positive if they contain at least one region of interest containing a particular person, and negative if the video track does not contain a region of interest containing the particular person. Visual similarity models are trained using the positive bags.