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Part-Level Fully Convolutional Networks for Pedestrian Detection

Abstract: 

Since pedestrians in videos have a wide range of appearances such as body poses, occlusions, and complex backgrounds, pedestrian detection is a challengeable task. In this paper, we propose part-level fully convolutional networks (FCN) for pedestrian detection. We adopt deep learning to deal with the proposal shifting problem in pedestrian detection. First, we combine convolutional neural networks (CNN) and FCN to align bounding boxes for pedestrians. Then, we perform part-level pedestrian detection based on CNN to recall the lost body parts. Experimental results demonstrate that the proposed method achieves 6.83% performance improvement in log-average miss rate over CifarNet.

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Paper Details

Authors:
Xinran Wang,Cheolkon Jung,Alfred O Hero
Submitted On:
8 March 2017 - 10:41pm
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Type:
Presentation Slides
Event:
Document Year:
2017
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[1] Xinran Wang,Cheolkon Jung,Alfred O Hero, "Part-Level Fully Convolutional Networks for Pedestrian Detection", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1570. Accessed: Dec. 16, 2017.
@article{1570-17,
url = {http://sigport.org/1570},
author = {Xinran Wang;Cheolkon Jung;Alfred O Hero },
publisher = {IEEE SigPort},
title = {Part-Level Fully Convolutional Networks for Pedestrian Detection},
year = {2017} }
TY - EJOUR
T1 - Part-Level Fully Convolutional Networks for Pedestrian Detection
AU - Xinran Wang;Cheolkon Jung;Alfred O Hero
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1570
ER -
Xinran Wang,Cheolkon Jung,Alfred O Hero. (2017). Part-Level Fully Convolutional Networks for Pedestrian Detection. IEEE SigPort. http://sigport.org/1570
Xinran Wang,Cheolkon Jung,Alfred O Hero, 2017. Part-Level Fully Convolutional Networks for Pedestrian Detection. Available at: http://sigport.org/1570.
Xinran Wang,Cheolkon Jung,Alfred O Hero. (2017). "Part-Level Fully Convolutional Networks for Pedestrian Detection." Web.
1. Xinran Wang,Cheolkon Jung,Alfred O Hero. Part-Level Fully Convolutional Networks for Pedestrian Detection [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1570