Visual Tracking with Convolutional Neural Network

Visual Tracking is a fundamental task in computer vision which has been extensively researched. Though much progress exists in literature, it is still very challenging due to factors such as partial occlusions, pose variations, viewpoint variations and so on. In this paper, we address the visual tracking problem in a discriminant manner where a simple convolutional neural network (CNN) is employed to extract discriminant features and simultaneously classify the object from the background. The effectiveness of the proposed method is validated on a comprehensive evaluation involving 10 challenging video sequences and five state-of-the-art trackers.