Representative Frame Selection for Object Re-identification
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Solution Overview
Problem
Existing methods for object re-identification across different camera views face challenges due to varying lighting conditions, similar visual appearances of objects, and the complexity of handling multiple frames, which can lead to increased computation and reliability issues, especially in crowded scenes.
Innovation Solution
A method that selects representative frames from each camera view based on intra-camera object appearance distances, allowing for efficient re-identification by comparing these representative frames to determine if a candidate object matches a query object across camera views.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple frames are used for object re-identification, then matching accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent extracts only the most representative frames from the video sequence based on motion activity analysis and landmark detection. Instead of processing all frames, it selectively extracts key frames that contain the most useful information for re-identification, thereby reducing computational complexity while maintaining matching accuracy.
Solution Approach 2:
The patent applies partial action by using a subset of frames (landmark frames) rather than the complete set. By identifying and using only the critical frames that capture important object states, it achieves effective re-identification with reduced computational burden.
2Reliability
If multiple frames are compared for re-identification, then reliability is improved, but the process becomes more complex and prone to confusion from outlier frames
Solution Approach 1:
The patent performs preliminary action by pre-processing frames to identify and remove outliers before the actual matching process. It uses motion activity analysis and landmark frame detection to prepare a clean set of representative frames, which prevents confusion from outlier frames during re-identification and simplifies the overall process.
Solution Approach 2:
The patent incorporates feedback mechanisms through motion activity analysis, where the system continuously monitors frame quality and selects landmark frames based on detected motion patterns. This feedback loop ensures that only high-quality, representative frames are used for matching, improving reliability while maintaining process simplicity.
3Measurement precision
If frames are selected based on motion activity, then landmark detection accuracy is improved, but background motion and occlusion reduce reliability
Solution Approach 1:
The patent applies local quality by focusing motion activity analysis on specific regions of interest (such as the object's body or key features) rather than the entire frame. This localized approach improves landmark detection accuracy while being less sensitive to background motion and occlusion affecting other areas.
Solution Approach 2:
The patent segments the frame into relevant regions and analyzes motion activity separately in each region. By dividing the scene into object-specific areas and background areas, it can accurately detect landmarks based on object motion while ignoring or downweighting background motion and occlusion effects.
Data Source
AI summary
Method, system, apparatus and computer readable medium for re-identifying a query objection moving from a first camera view to a second camera view. The method comprises the steps of accessing a first set of image frames (Q) captured from the first camera view and a second set of image frames (C) captured from the second camera view; selecting a first representative frame (QR) from the first set of image frames and a second representative frame (CR) from the second set of image frames based on a plurality of intra-camera object appearance distances between frame pairs in each set of image frames; and re-identifying the candidate object in the second camera view as the query object in the first camera view based on at least one inter-camera object appearance distance (D) calculated between the first representative frame and the second representative frame.