Multi-feature real time pedestrian detection from dense stereo SORT-SGM reconstructed urban traffic scenarios

摘要

In this paper, a real-time system for pedestrian detection in traffic scenes is proposed. It takes the advantage of having a pair of stereo video-cameras for acquiring the image frames and uses a sub-pixel level optimized semi-global matching (SORT-SGM) based stereo reconstruction for computing the dense 3D points map with high accuracy. A multiple paradigm detection module considering 2D, 3D and optical flow information is used for segmenting the candidate obstacles from the scene background. Novel features like texture dissimilarity, humans' body specific features, distance related measures and speed are introduced and combined in a feature vector with traditional features like HoG score, template matching contour score and dimensions. A random forest (RF) classifier is trained and then applied in each frame for distinguishing the pedestrians from other obstacles based on the feature vector. A k-NN algorithm on the classification results over the last frames is applied for improving the accuracy and stability of the tracked obstacles. Finally, two comparisons are made: first between the classification results obtained by using the new SORT-SGM and the older local matching approach for stereo reconstruction and the second by comparing the different features RF classification results with other classifiers' results.