Pedestrian detection by fusing 3D points and color images

In this paper, a fusing approach of a 3D sensor and a camera are used to improve the reliability of pedestrian detection. The proposed pedestrian detecting system adopts DBSCAN to cluster 3D points and projects the candidate clusters onto images as region of interest (ROI). Those ROIs are detected by HOG (histograms of oriented gradients) pedestrian detector. Because the DBSCAN groups together 3D points and rejects outlier points correctly, the proposed system has a low false detection rate. The performance is also improved since the proposed system only detects the ROI instead of the whole color image.