A Part-Based Gaussian Reweighted Approach for Occluded Vehicle Detection

Yu HUANG  Zhiheng ZHOU  Tianlei WANG  Qian CAO  Junchu HUANG  Zirong CHEN  

Publication
IEICE TRANSACTIONS on Information and Systems   Vol.E102-D   No.5   pp.1097-1101
Publication Date: 2019/05/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2018EDL8257
Type of Manuscript: LETTER
Category: Pattern Recognition
Keyword: 
vehicle detection,  occlusion,  reweight,  

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Summary: 
Vehicle detection is challenging in natural traffic scenes because there exist a lot of occlusion. Because of occlusion, detector's training strategy may lead to mismatch between features and labels. As a result, some predicted bounding boxes may shift to surrounding vehicles and lead to lower confidences. These bounding boxes will lead to lower AP value. In this letter, we propose a new approach to address this problem. We calculate the center of visible part of current vehicle based on road information. Then a variable-radius Gaussian weight based method is applied to reweight each anchor box in loss function based on the center of visible part in training time of SSD. The reweighted method has ability to predict higher confidences and more accurate bounding boxes. Besides, the model also has high speed and can be trained end-to-end. Experimental results show that our proposed method outperforms some competitive methods in terms of speed and accuracy.