Home // International Journal On Advances in Intelligent Systems, volume 8, numbers 3 and 4, 2015 // View article

Pedestrian Detection with Cascaded Part Model for Occlusion Handling

Authors:
Yawar Rehman
Irfan Riaz
Fan Xue
Piao Jingchun
Jameel Ahmed Khan
Shin Hyunchul

Keywords: Pedestrian detection; occlusion handling

Abstract:
Pedestrian detection in a crowded environment under occlusion constraint is a challenging task. We have addressed this task by exploiting the properties of a rich feature set, which gives almost all cues necessary for recognizing pedestrians. Such rich feature set results in higher dimensional feature space. We have used partial least square regression to map these higher dimensional features to a lower dimensional yet discriminative feature space. Part model is further applied to deal with occlusions. The proposed method gives the best reported results on INRIA pedestrian dataset with detection accuracy of 98% at 10-4 False Positives Per Window (FPPW) and a miss rate of 31.62% at 10-1 False Positives Per Image (FPPI). We have also demonstrated the effectiveness of our part model under partial and heavily occluded conditions. Our proposed system outperforms several state of the art techniques under various evaluation conditions of INRIA pedestrian database.

Pages: 426 to 436

Copyright: Copyright (c) to authors, 2015. Used with permission.

Publication date: December 30, 2015

Published in: journal

ISSN: 1942-2679