Deep bidirectional correlation filters for visual object tracking

Visual Object Tracking (VOT) is an essential task for many computer vision applications. VOT becomes challenging when a target object faces severe occlusion, drastic illumination changes, and scale variation problems. In the literature, Discriminative Correlation Filters (DCFs)-based tracking methods have achieved promising results in terms of accuracy and efficiency in many complex VOT scenarios. A plethora of DCFs trackers have been proposed which exploit information observed in past frames to create and update DCFs for VOT. To adapt to target appearance variations, the DCFs are enhanced by incorporating spatial and temporal consistency constraints. Nevertheless, the performance degradation is observed for these methods because of the aforementioned limitations. To address these issues, we propose a novel algorithm based on bidirectional DCFs for VOT. In this algorithm, we propose the original idea of leveraging information from both past and future frames. The proposed algorithm first tracks the target object forward in the video sequence and then its uses the predicted location of the last window frame and track the target object backward towards the current frame. We design an appearance consistency loss function by taking the L₂ norm between the regression target of the forward tracking and response map of the backward tracking to obtain the resulting response map. Our proposed algorithm realizes a highly accurate DCFs because forward and backward tracking information are fused together for consistent VOT. Although, a result will be output with some small delay because information is taken from a future to the present period, our proposed algorithm has the merit of addressing the drastic appearance variations VOT challenges. We evaluate our proposed tracker using deep features on three publicly available challenging datasets. Our results demonstrate the superior performance of the proposed tracker compared to the existing state-of-the-art trackers.