Tracking Moving Objects: A Comparative Study

Visual tracking is considered to be one of the most important challenges in computer vision with numerous applications such as object recognition and detection. In the present paper, four tracking techniques will be introduced circulant structure with kernels (CSK), Kernelized correlation filters (KCF), Adaptive color attributes (ACT), and distractor awareness tracker (DAT) for the visual object tracking (VOT14) challenge datasets. Performance evaluation for each method was calculated using three measures, center location error (CLE), distance precision (DP), and speed in frames per second (FPS). Results have shown that KCF tracker is the fastest technique. It achieves the best results in most sequences and the highest precision at lower threshold. It is used in time-critical application with satisfactory performance. Each tracker performs favorable and competitive results in some sequence and fails in others. So it is noted that the choice of the tracker is application dependent.