Reliable Temporally Consistent Feature Adaptation for Visual Object Tracking

Abstract

Correlation Filter (CF) based trackers have been the frontiers on various object tracking benchmarks. Use of multiple features and sophisticated learning methods have increased the accuracy of tracking results. However, the contribution of features are often fixed throughout the video sequence. Unreliable features lead to erroneous target localization and result in tracking failures. To alleviate this problem, we propose a method for online adaptation of feature weights based on their reliability. Our method also includes the notion of temporal consistency, to handle noisy reliability estimates. The two objectives are coupled to model a convex optimization problem for robust learning of feature weights. We also propose an algorithm to efficiently solve the resulting optimization problem, without hindering tracking speed. Results on VOT2018, TC128 and NfS30 datasets show that proposed method improves the performance of baseline CF trackers.