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Accurate human tracking in surveillance scenes is one of the preliminary requirements for other tasks. However, when the human target is small, the extracted features may not be prominent and thus the tracking performance is unsatisfactory. The colour feature is relatively robust to the change of target size and shape, but it is prone to be affected by the background information. For the above reasons, the authors introduce random walker segmentation into human tracking and determine the background region according to the distribution characteristics of segmentation results. Even if the colour of the target is very similar to that of the background, this algorithm can segment the target. Furthermore, the principal component analysis method is used to distinguish human targets from the background as well. During tracking, the authors prevent the degradation of the target model by adding new target information. In order to overcome the mean‐shift local optimisation problem, the authors search for the candidate target region with the largest weight according to the sum of all probability in each region. Experimental results further show that the authors’ tracking algorithm demonstrates better performance on tracking small human target under some challenging scenes compared with several existing tracking methods.

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