Visible to the public On-Road Multiple Obstacles Detection in Dynamical Background

TitleOn-Road Multiple Obstacles Detection in Dynamical Background
Publication TypeConference Paper
Year of Publication2014
AuthorsJing Li, Ming Chen
Conference NameIntelligent Human-Machine Systems and Cybernetics (IHMSC), 2014 Sixth International Conference on
Date PublishedAug
Keywordsauto-adapted algorithm, Cameras, Computer vision, dynamical background, feature extraction, feature extraction strategy, fuzzy attribution, fuzzy decision function, fuzzy decision fusion method, fuzzy neural nets, fuzzy neural network, Fuzzy neural networks, histogram-based segmentation, homography, image alignment, image classification, inverse perspective mapping, IPM image, lane markings, object detection, obstacle classification probability, on-road multiple obstacle detection, pedestrians, pedestrians detection, Radar, road plane, road vehicle, Roads, stabilized vanishing point, temporal filtering, Vehicles

Road In this paper, we focus on both the road vehicle and pedestrians detection, namely obstacle detection. At the same time, a new obstacle detection and classification technique in dynamical background is proposed. Obstacle detection is based on inverse perspective mapping and homography. Obstacle classification is based on fuzzy neural network. The estimation of the vanishing point relies on feature extraction strategy, which segments the lane markings of the images by combining a histogram-based segmentation with temporal filtering. Then, the vanishing point of each image is stabilized by means of a temporal filtering along the estimates of previous images. The IPM image is computed based on the stabilized vanishing point. The method exploits the geometrical relations between the elements in the scene so that obstacle can be detected. The estimated homography of the road plane between successive images is used for image alignment. A new fuzzy decision fusion method with fuzzy attribution for obstacle detection and classification application is described. The fuzzy decision function modifies parameters with auto-adapted algorithm to get better classification probability. It is shown that the method can achieve better classification result.

Citation Key6917316