An Improved Meanshift Tracking Algorithm Using Adaptive Quantization Step in Color Space

The traditional meanshift based tracking algorithm uses a constant quantization step to carry out feature generation in the color space but it cannot dynamically alter the quantization step with the changes of the target geometry to improve computational efficiency in large depth-of-field scenarios. Based on the traditional meanshift algorithm, we proposed a tracking algorithm using adaptive quantization step which automatically adjusts the quantization step of the color histogram and uses the dynamic time warping algorithm to match the features with different dimensions when the target geometry changes, thereby, effectively reducing the average frame processing time. The comparative experiments under multiple scenarios demonstrated that the proposed algorithm can adaptively adjust the quantization step of color histogram in large depth of field scenarios and improve the operating efficiency of the algorithm.