关键词: 目标跟踪, 结构先验, 密集采样, 傅里叶变换

Abstract: In general, the multi-target, multi-frame data association needs to be exploited in multiple objects tracking. However, large number of samples and time complexity of the objective function limits its application. A novel real-time multi-object tracking algorithm is proposed. An on-line updating structure priors model is presented to capture the data space association. For the inference of space confidence, a dense sampling method is presented to overcome the imprecision of target state caused by traditional sparse sampling strategy. The surrounding regions of target are exacted as positive example, and then transformed circularly as negatives. Using the well-established theory of Circulant matrices, a fast learning method is achieved to infer the confidence for all possible position of target with the Fast Fourier Transform. Experimental results on a number of challenging sequences demonstrate the precision and efficiency of the method.

Key words: object tracking, structure priors, dense sampling, fast fourier transform