Optimal location estimation using a metaheuristic algorithm and deep learning for visual tracking

In the last decade, it is noted that visual tracking is the most an active topic in computer vision field which results in the appearance of several visual trackers. Although its efficiency, the existing trackers fail to handle challenging appearance changes of the tracked object in complex image sequences, such as significant deformation, fast motion, occlusions... etc. In this paper, we address the pre-cited problems. We propose a novel method for robust location estimation in a tracking. The proposed method base on two main steps. First, we learn adaptively five correlation filters in the spatial domain in order to improve tracking accuracy and efficiency. Indeed, Bat algorithm (BA) is a suitable tool for solving the update model equation of the correlation filters. Second, we adaptively learn these models on hierarchical convolutional features to encode the target appearance. Extensive experiments on a reference dataset, justify the robustness of the proposed method.