Single Object Trackers in OpenCV: A Benchmark
Object tracking is one of the fundamental tasks in computer vision. It is used almost everywhere: human-computer interaction, video surveillance, medical treatments, robotics, smart cars, etc. Many object tracking methods have been published in recent scientific publications. However, many questions still remain unanswered, such as, which object tracking method to choose for a particular application considering some specific characteristics of video content or which method will perform the best (quality-wise) and which one will have the best performance? In this paper, we provide some insights into how to choose an object tracking method from the widespread OpenCV library. We provide benchmarking results on the OTB-100 dataset by evaluating the eight trackers from the OpenCV library. We use two evaluation methods to evaluate the robustness of each algorithm: OPE and SRE combined with Precision and Success Plot.