Neural Network Object Tracker Failure Detection
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Solution Overview
Problem
Conventional object tracking systems using artificial neural networks often fail to detect tracking failures efficiently, particularly due to unstable lighting, erratic movement, clutter, and occlusions, leading to prolonged time in identifying and recovering from tracking errors.
Innovation Solution
A method and apparatus that utilize a failure detection network to classify activations from an intermediate layer of the object tracking network, determining whether to initiate a recovery mode or remain in tracking mode based on the classification, thereby reducing the time to detect tracking failures and improving the accuracy of object tracking.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional trajectory difference detection is used to identify tracking failures, then the system can detect when target location substantially changes, but it produces false positives and requires fine-tuning of ad hoc thresholds for each tracker setting
Solution Approach 1:
The patent introduces an intermediary mechanism (failure detection network or alternative detection metrics) that mediates between the tracker output and failure detection, eliminating the need for manual threshold tuning while improving detection reliability. This intermediary layer processes tracker predictions and objectively determines failure states without requiring ad hoc threshold settings.
Solution Approach 2:
The system implements feedback by continuously monitoring tracker performance and using detected failures to adjust or reinitialize the tracker. This closed-loop feedback mechanism automatically adapts to different tracker settings and conditions, eliminating the need for manual threshold tuning for each configuration.
2Reliability
If appearance difference detection is used to identify tracking failures, then the system can detect when target appearance substantially changes, but it results in false positives when target appearance changes due to illumination and pose changes
Solution Approach 1:
The patent segments the failure detection task into multiple independent metrics (trajectory difference, appearance difference, confidence score) that are evaluated separately. This segmentation allows the system to cross-validate results and reduce false positives caused by illumination or pose changes, as not all metrics will simultaneously indicate failure in normal variation scenarios.
Solution Approach 2:
The system changes the parameters used for failure detection by incorporating multiple different metrics (spatial trajectory, temporal appearance, confidence levels) rather than relying on a single appearance difference metric. This multi-parameter approach enables the system to distinguish between genuine tracking failures and normal appearance variations due to lighting or pose changes.
3Reliability
If the system re-initializes the object tracker to recover a lost target, then the target can be recovered, but it increases the time required to detect and recover from tracking failures
Solution Approach 1:
The patent implements preliminary action by continuously monitoring multiple failure indicators and detecting failures earlier in the tracking process. By using proactive failure detection through multiple metrics before complete target loss occurs, the system can initiate recovery procedures sooner, reducing the overall time lost to tracking failures while maintaining reliable target recovery.
Data Source
AI summary
A method of detecting failure of an object tracking network with a failure detection network includes receiving an activation from an intermediate layer of the object tracking network and classifying the activation as a failure or success. The method also includes determining whether to initiate a recovery mode of the object tracking network or to remain in a tracking mode of the object tracking network, based on the classifying.