Adaptive spatial-temporal surrounding-aware correlation filter tracking via ensemble learning

•A novel adaptive object tracking method using feature fusion with a multi-expert tracking scheme is presented.•The surrounding patches around the target are introduced in the filter learning stage to reduce tracking drift caused by external distractions.•A selective spatial regularizer is introduced to reduce the boundary effects and a temporal regularizer that considers present and last frames.•Experiments on several benchmarks show that the proposed tracker achieves promising results compared to other modern trackers. With the advancement of computer vision, object trackers based on discriminative correlation filters (DCF) have demonstrated superior performance and accuracy compared to traditional trackers. However, most existing DCF-based trackers are easily affected by various factors, such as cluttered background, illumination variations, occlusions, rotations etc. Therefore, in order to accurately track the target, further investigation into the characteristics of the correlation filter is required. In this study, we propose an adaptive spatial-temporal surrounding-aware correlation filter tracker via the ensemble learning (ASTSAELT) technique. Specifically, the adaptive spatial-temporal regularized correlation filter to remove the boundary effects and temporal degradation is presented. And then, a method of extracting surrounding samples according to the size and shape of the tracking object, designed to preserve the integrity of the object, is proposed. Moreover, our tracker utilizes a multi-expert tracking framework to improve its performance by integrating both handcrafted features and deep convolutional layer features. And then, the update strategy is proposed to measure the reliability of the current tracking result and mitigate model corruption. Finally, numerous experiments on visual tracking benchmarks including OTB2013, OTB2015, TempleColor128, UAV123, UAVDT and DTB70 are implemented to verify the developed method achieves superior performance compared with several state-of-the-art methods.