Particle filter‐based vehicle tracking via HOG features after image stabilisation in intelligent drive system

Abstract

Tracking vehicles automatically are of great importance in the intelligent transportation systems to guarantee the traffic safety, especially for the self-driving technique. To solve the problem of the tiny jitter of the camera due to the shaking platform and the rough road, and to track the vehicle ahead, a salient feature-based video stabilisation method is introduced to remove the effect of camera motion, and an improved particle filter-based vehicle tracking algorithm via histogram of oriented gradient (HOG) features is proposed in this study. For video stabilisation, first, the features from accelerated segment test algorithm are applied to extract salient points from the two adjacent frames. Then, the fast retina keypoint algorithm is used to match the correspondences and the M-estimator sample consensus algorithm is applied to remove the outliers. Finally, the image with tiny jitter is stabilised by the affine transformation matrix calculated from the retained inliers. After the image stabilisation, an improved particle filter-based tracking method is proposed for vehicle tracking via the HOG feature extracted from an adaptive searching area determined by the expected vehicle position of the previous frame. Experimental results demonstrate that camera motion can be effectively compensated by the feature-based stabilisation method, and the forward vehicle can be tracked with stability and robustness in real time.