Robust single object tracker based on kernelled patch of a fixed RGB camera

One of the weaknesses of the mean-shift tracker is its limited ability to track a fast-moving object. Not only does the captured image look blurred but the object will often be out of the search window. Moreover, blur issue is a normal occurrence for a fast-moving object by using a commercially available camera due to a low frame rate. This paper aims to develop a robust observation detector for a single object tracker, especially for blurred image, fast-moving and non-rigid object. We propose a fusion of a kernel-based histogram and feature detector based approach. The measurement input is selected from candidate patches that will be generated by matching the vector descriptor. These vectors are built based on the detected points of interest. Two colour spaces are considered where all three channels of RGB are used, while only the hue channel of the HSV space is utilized. The kernel method is employed for better histogram accumulation. The output patch of the previous frame will be the target model where histogram similarity is measured based on Gaussian distribution. The selected output of the patch matching will undergo position smoothing for better precision. Maximum likelihood approach is used to iterate the patch position until the best match is found. The results indicate that our algorithm has a good detection for challenging surroundings and environments. In some cases, our algorithm may have less accuracy but in no case did it failed to detect.