Random walks colour histogram modification for human tracking

Abstract

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.

An Intelligent Automatic Human Detection and Tracking System Based on Weighted Resampling Particle Filtering

“…Zhang et al [14] proposed a robust 3D human pose tracking approach from silhouettes using a likelihood function. Zhao et al [15] used a principal component analysis to extract features from color and use them in a random walker segmentation algorithm to assist human tracking.…”

“…A dynamic model is represented by Equation 14, which propagates the sample; the deterministic component of the model is represented by A. The target human size and position can be determined from the estimated vector using the weight of every sample and its state vector, as shown in Equation (15). To update the weight of each sample, Bhattacharyya distance is used and is shown in Equation 16.…”

Effective appearance model update strategy in object tracking

“…Object tracking is a remarkable and rapid development field in computer vision, and it involves many challenging research focus and often entails with other computer vision problems, such as human-computer interaction, video surveillance, automatic drive, mobile robots and so on [1][2][3]. It can be defined as the task of localising an object in each frame which is annotated by the user at the beginning of a video sequence [4].…”