An improved mean-shift tracker with kernel prediction and scale optimisation targeting for low-frame-rate video tracking

Abstract

The mean-shift (MS) algorithm is widely used in object tracking because of its speed and simplicity. However, it assumes certain overlap of object appearance and smooth change in object scale between consecutive video frames. This assumption is usually violated in a low-frame-rate (LFR) video, which contains fast motion and scale changes. An LFR video is widely adopted in applications such as surveillance systems, where real-time object tracking is highly desirable but the traditional MS algorithm does not perform well. We addressed this problem by proposing a novel and enhanced mean-shift tracker, named SMDShift, that uses kernel prediction and stochastic meta-descent (SMD) optimization method to deal with the kernel position and scale variation when tracking objects in an LFR video. In our experiments, the SMDShift can track fast moving objects with significant scale change in an LFR video sequence on which the traditional mean-shift and Camshift algorithms fail.