PT Unknown
AU Alejandro Gonzalez Alzate
   Gabriel Villalonga
   German Ros
   David Vazquez
   Antonio Lopez
TI 3D-Guided Multiscale Sliding Window for Pedestrian Detection
BT Pattern Recognition and Image Analysis, Proceedings of 7th Iberian Conference , ibPRIA 2015
PY 2015
BP 560
EP 568
VL 9117
DI 10.1007/978-3-319-19390-8_63
DE Pedestrian Detection
AB The most relevant modules of a pedestrian detector are the candidate generation and the candidate classification. The former aims at presenting image windows to the latter so that they are classified as containing a pedestrian or not. Much attention has being paid to the classification module, while candidate generation has mainly relied on (multiscale) sliding window pyramid. However, candidate generation is critical for achieving real-time. In this paper we assume a context of autonomous driving based on stereo vision. Accordingly, we evaluate the effect of taking into account the 3D information (derived from the stereo) in order to prune the hundred of thousands windows per image generated by classical pyramidal sliding window. For our study we use a multimodal (RGB, disparity) and multi-descriptor (HOG, LBP, HOG+LBP) holistic ensemble based on linear SVM. Evaluation on data from the challenging KITTI benchmark suite shows the effectiveness of using 3D information to dramatically reduce the number of candidate windows, even improving the overall pedestrian detection accuracy.
ER