Optimal colour‐based mean shift algorithm for tracking objects - Xiaowei An; Jaedo Kim; Youngjoon Han (IET Computer Vision, 2014)
The mean‐shift method is widely used to locate a target object quickly in sequential images. The mean‐shift algorithm takes advantage of a colour distribution with a uniform quantisation. However, the quantisation method ignores the close relationship of colour statistics. The uniform distribution also results in a colour histogram with many empty bins, which introduces additional computation cost in the tracking procedure. To reduce the number of these redundant, empty bins, the authors present a new optimal colour‐based, mean‐shift algorithm for tracking objects. In the proposed method, the optimal colours are extracted by a histogram agglomeration, which clusters three‐dimensional (3D) colour histogram bins with the frequency ratios of 3D colour values. After obtaining optimal colours in a RGB colour histogram, the target image is represented by the indices of the optimal colours. The mean‐shift algorithm thus creates a confidence map in a candidate image based on the optimal colour histogram in the target image. It then finds the peak of the confidence map near the previous position of an object area. Comparative experiments with the conventional mean‐shift method showed that our method has the advantages of decreased processing time and improved tracking accuracy.