Video Object Re-Identification with Contextual Feature Weighting
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Solution Overview
Problem
Traditional object re-identification algorithms in camera surveillance lack the ability to adapt to changes in the distribution of objects within a monitored scene, leading to inefficiencies in distinguishing objects based on their features.
Innovation Solution
A method for weighting features in a feature vector based on their deviation from a reference model that represents the current context of the scene, assigning higher weights to features that deviate more from the reference model, thereby enhancing the adaptability of object re-identification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional object re-identification algorithms are used, then object tracking can be performed, but the algorithms cannot adapt to changes in object distribution in the monitored scene
Solution Approach 1:
The patent implements dynamic adaptation by continuously updating the reference model with recently detected objects in the scene. The system adjusts feature weights based on current scene characteristics rather than using fixed pre-trained parameters, allowing the re-identification algorithm to adapt to changing object distributions while maintaining reliability through evidence-based weight adjustment.
Solution Approach 2:
The patent changes the parameters of feature vectors by introducing context-dependent weighting. Each feature is assigned a weight based on its discriminative power in the current scene context, derived from comparing against the reference model. This parameter transformation allows the same feature vector to be effectively adapted to different scene conditions.
2Measurement precision
If all features in the feature vector are treated equally, then the algorithm is simple to implement, but important distinguishing features cannot be emphasized
Solution Approach 1:
The patent applies local quality by assigning different weights to different features within the feature vector based on their local importance in the current scene context. Features that provide better discrimination between objects in the specific scene context receive higher weights, while less discriminative features receive lower weights, optimizing measurement precision without excessive complexity.
Solution Approach 2:
The system uses feedback from the reference model to automatically adjust feature weights. By comparing detected objects against the reference model and measuring deviation, the system generates weight assignments that reflect which features are most discriminative in the current context, eliminating the need for manual feature selection while improving precision.
3Reliability
If features similar to the majority of objects in the scene are used for re-identification, then common features can be detected, but unique distinguishing features are lost
Solution Approach 1:
The patent inverts the traditional approach by not selecting features based on their presence in the majority of objects, but rather by selecting features based on their deviation from the majority. Features that show significant deviation from the reference model (representing common objects) are assigned higher weights, thereby preserving and emphasizing unique distinguishing features while maintaining reliable object distinction.
Data Source
AI summary
The present disclosure generally relates to a method for weighting of features in a feature vector of an object detected in a video stream capturing a scene, comprising: determining a feature vector comprising a set of features for a detected object in the video stream; acquiring a reference feature vector of a reference model of the scene; and assigning a weight to at least one feature of the determined feature vector, wherein the weight for a feature of the determined feature vector depends on a deviation measure indicative of a degree of deviation of the feature from a corresponding feature of the acquired reference feature vector of the reference model.