Adaptive Threshold for Online Object Recognition and Re-identification Tasks

TL;DR

This paper introduces an adaptive thresholding method for online object recognition and re-identification tasks, significantly improving accuracy over fixed thresholds in dynamic and imbalanced scenarios.

Contribution

It proposes a novel online optimization-based adaptive thresholding technique that adjusts decision boundaries dynamically for better performance in changing environments.

Findings

Achieved 12-45% accuracy improvement over fixed thresholds

Validated on LFW and athletes datasets

Effective in imbalanced and dynamic database conditions

Abstract

Choosing a decision threshold is one of the challenging job in any classification tasks. How much the model is accurate, if the deciding boundary is not picked up carefully, its entire performance would go in vain. On the other hand, for imbalance classification where one of the classes is dominant over another, relying on the conventional method of choosing threshold would result in poor performance. Even if the threshold or decision boundary is properly chosen based on machine learning strategies like SVM and decision tree, it will fail at some point for dynamically varying databases and in case of identity-features that are more or less similar, like in face recognition and person re-identification models. Hence, with the need for adaptability of the decision threshold selection for imbalanced classification and incremental database size, an online optimization-based statistical feature learning adaptive technique is developed and tested on the LFW datasets and self-prepared athletes datasets. This method of adopting adaptive threshold resulted in 12-45% improvement in the model accuracy compared to the fixed threshold {0.3,0.5,0.7} that are usually taken via the hit-and-trial method in any classification and identification tasks. Source code for the complete algorithm is available at: https://github.com/Varat7v2/adaptive-threshold