Occlusion-Aware Correlation Particle Filter Target Tracking Based on RGBD Data

Abstract

In recent decades, there have been considerable improvements in target-tracking algorithms. However, aspects such as target occlusion, scale variation, and illumination changes still present significant challenges to existing algorithms. In this paper, we describe an occlusion-aware correlation particle filter target-tracking method based on RGBD data. First, we derive a target occlusion judgment mechanism based on a depth image and the histogram of oriented gradients (HOG) feature. We then formulate the tracking mechanism for the target prediction–tracking–optimization–redetection process using a correlation maximum likelihood estimation particle filter algorithm. We propose an adaptive update strategy whereby the system saves a well-tracked model when no occlusion occurs, and then uses this saved model to replace poorly tracked models in the event of occlusion. Furthermore, we consider the scale variation and adjust the target size according to the depth image, but we leave the HOG feature vector dimension of the target area unchanged. Thus, the problems such as model offset, scale variation, and loss of features are corrected over time. The experimental results demonstrate that the proposed target-tracking algorithm can detect target occlusion and track targets well, requires fewer calculations to perform target prediction–tracking–optimization–redetection, reduces the impact of illumination changes, and achieves better real-time performance and accuracy than many existing algorithms.