Combining multiple complementary features for pedestrian and motorbike detection.
Journal
IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC
Pages
1358-1363
Date Issued
2013
Author(s)
Wu, Cheng-En
Chan, Yi-Ming
Hsiao, Pei-Yung
Huang, Shih-Shinh
Chen, Han-Hsuan
Huang, Pang-Ting
Hu, Shao-Chung
Abstract
Pedestrian and motorbike detection are two important areas in obstacle detection on road. Most state-of-the-art detectors are constructed with new features or learning methods on Histograms of Oriented Gradients (HOG) features. However, few researches focus on analyzing which features are complementary for the aforementioned detection. According to our study of pedestrians and motorbikes, there are three major properties including shape, texture, and self-similarity. We design a Shape, Texture and Self-Similarity (STSS) feature for these properties. The features we have employed here are HOG, Local Oriented Pattern (LOP), Color Self-Similarity (CSS), and Texture Self-Similarity (TSS). The STSS detector which combines Shape, Texture, and Self-Similarty features achieves 31% log-average miss rate. At the same time, 93% detection rate at 10-4 false positives per window on INRIA Person Dataset has also been concluded. Besides, we also have evaluated our detector on Caltech Motorbike Dataset and Caltech Pedestrian Dataset, and found the detector outperforms HOG detector in these datasets. As a result, we have shown that these features are complement to each other and useful in pedestrian and motorbike detection. © 2013 IEEE.
Event(s)
2013 16th International IEEE Conference on Intelligent Transportation Systems: Intelligent Transportation Systems for All Modes, ITSC 2013
Type
conference paper