Location classification of detected pedestrian

Abstract

This paper proposes a method to detect pedestrians from a single camera mounted on the vehicle then classify the location of the pedestrian to give information for the driver assistance system. The system consists of three stages. First, moving objects are detected using optical flows method. A moving object is extracted from the relative motion by segmenting the region representing the same optical flows after compensating the ego-motion of the camera. The regions of moving object are detected as transformed objects which are different from the previously registered background. Second, histogram of oriented gradients (HOG) features descriptor and linear support vector machine (SVM) are used to recognize the pedestrian from detected moving objects. Third, a heuristic method according to the image formation in advance from its geometrical coordinates is proposed. It is used for classify the location of the detected pedestrian using the region properties of the image. The image is classified into two regions, the road region in front of vehicle and the pedestrian movement region. The proposed method is evaluated using sequential images in outdoor environment, and the performance results shown the best pedestrian detection rate is 99.3% at 0.09 false positive rate. The location classification evaluation shown correct detection rate is 92.40%.

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Cite this paper

@article{Hariyono2014LocationCO, title={Location classification of detected pedestrian}, author={Joko Hariyono and Van-Dung Hoang and Kang-Hyun Jo}, journal={2014 14th International Conference on Control, Automation and Systems (ICCAS 2014)}, year={2014}, pages={599-602} }