On-Road Obstacle Detection and Tracking with Environment Information

To detect people in a video sequence robustly is hard due to various challenges. One of the most successful discriminative features for finding people goes to the Histogram of Oriented Gradients (HOGs). Although the major contour information is encoded in the HOG feature well, background clutter disturbs the gradient information. Thus, an extension of the HOGs, called histogram of oriented of gradient of granules, is proposed. Instead of collecting gradient information over each pixel, the histograms of gradients of small regions are computed. The clutter background problem can be solved by encoding extra region information. A robust system for detecting on-road multiple vehicles and multiple lanes while integrating both lane and vehicle information is designed. Most researches so far can only detect single/multiple lanes or vehicles separately. To achieve more reliable results, the relationship between lane and vehicle which can support detection of either of them should be modeled. Following this, we thus integrate spatial and temporal information of lanes and vehicles through employment of the probabilistic data association filter model. Such integration will improve the consistency of vehicle and lane tracking, and hence increase the performance of on-road vehicle detection. The experiments have validated our hereby proposed system for detecting multiple vehicles and multiple lanes satisfactorily and reliably. To robustly detect people and vehicle on the road in a video sequence is also a challenging problem. Most researches focus on detecting or tracking of specific targets only. Nevertheless, the performance of the system conceivably can be improved with the help of the geometry information. Thus, in this research, instead of detecting vehicle or pedestrian individually, a framework integrating the aforementioned heterogeneous information is proposed. Here, our approach let the system naturally integrate different information using the scene geometric information. The camera’s pitch angle is estimated with a novel vanishing point estimator. Instead of detecting the vanishing points using line intersection approach, the object information from tracker are also considered. Specifically, the detected vehicle or pedestrian will cast votes for the hypothesized horizon line. The vanishing line can be detected even when the scenes are cluttered or crowded, and thus the geometric information can be estimated under challenging circumstance. Such information of scene can help the system refine our detection results through Bayes’ network. Finally, to verify the performance of the system, comprehensive experiments have been conducted with the KITTI dataset. It is quite promising that the state-of-the-art detector, in our case, Regionlet detector, can be improved.