Learning multi-regularized mutation-aware correlation filter for object tracking via an adaptive hybrid model

Discriminative Correlation Filters (DCF) have emerged as a popular and effective approach in object tracking. With promising performance and efficiency, DCF-based trackers achieved impressive attention and reliable tracking results in several challenging scenarios. Although DCF-based trackers improve tracking performance, they still suffer from unexpected factors such as appearance mutations, filter degradation, and target distortion, which leads to decreased tracker performance. To address these challenges, a novel Multi-Regularized Mutation-Aware Correlation Filter (MRMACF) approach is presented. To do this, we propose a mutation-aware strategy with an adaptive hybrid model that employs the mutation threat mechanism technique to effectively handle the appearance mutations and filter degradation issues when the filter deviates from the target location. The mutation threat mechanism identifies sudden and significant changes in the target object's appearance, which is achieved by an adaptive hybrid model approach that compares the current appearance with recent historical models. Following that, we introduce an improved sparse spatial feature selection approach that incorporates row and column-based feature selection methods into the sparse spatial technique, which aims to identify crucial features within the target region and successfully address the problem of target distortion. Moreover, the surrounding-aware approach is presented that extracts the surrounding samples of the target region to utilize the context information, which prevents the filter deviation from the target and improves the discriminative ability. Specifically, the adaptive hybrid model approach is proposed to mitigate both tracking drift and the mutation threat of target by incorporating target position information from previous frames. Furthermore, we showcase the efficiency of the proposed MRMACF approach against existing modern trackers using the OTB-2013, OTB-2015, TempleColor-128, UAV-123, UAVDT, VOT-2018, LaSOT, and GOT-10K benchmark datasets. Specifically, our proposed method achieved the highest performance on the OTB-2015 dataset, with a DP score of 93.2% and an AUC score of 69.8%, respectively.