Author:
Qin Qin,Vychodil Josef, ,
Abstract
This paper proposes a new multi-feature detection method of local pedestrian based on a convolutional neural network (CNN), which provides a reliable basis for multi-feature fusion in pedestrian detection. According to the standard of pedestrian detection ratio, the pedestrian under the detection window would be segmented, using the sample labels to guide the local characteristics of CNN learning, the supervised learning after the network can obtain the local feature fusion more pedestrian description ability. Finally, a large number of experiments have been performed. The experimental results show that the local features of the neural network are better than those of most pedestrian features and combination features.
Publisher
Fuji Technology Press Ltd.
Subject
Artificial Intelligence,Computer Vision and Pattern Recognition,Human-Computer Interaction
Reference20 articles.
1. M. Szarvas, A. Yoshizawa, M. Yamamoto, et al., “Pedestrian detection with convolutional neural networks,” Intelligent Vehicles Symp., 2005 Proc. IEEE, pp. 224-229, 2005.
2. P. Sermanet, D. Eigen, X. Zhang, et al., “Overfeat: Integrated recognition, localization and detection using convolutional networks,” arXiv:1312.6229, 2013.
3. M. Szarvas, U. Sakai, and J. Ogata, “Real-time pedestrian detection using LIDAR and convolutional neural networks,” Intelligent Vehicles Symp., 2006 IEEE, pp. 213-218, 2006.
4. P. Sermanet, K. Kavukcuoglu, S. Chintala, et al., “Pedestrian detection with unsupervised multi-stage feature learning,” Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition, pp. 3626-3633, 2013.
5. C. Zhang and Z. Zhang, “Improving multiview face detection with multi-task deep convolutional neural networks,” Applications of Computer Vision (WACV), 2014 IEEE Winter Conf., pp. 1036-1041, 2014.
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