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Proceedings Paper

Pedestrian detection in infrared image using HOG and Autoencoder
Author(s): Tianbiao Chen; Hao Zhang; Wenjie Shi; Yu Zhang
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Paper Abstract

In order to guarantee the safety of driving at night, vehicle-mounted night vision system was used to detect pedestrian in front of cars and send alarm to prevent the potential dangerous. To decrease the false positive rate (FPR) and increase the true positive rate (TPR), a pedestrian detection method based on HOG and Autoencoder (HOG+Autoencoder) was presented. Firstly, the HOG features of input images were computed and encoded by Autoencoder. Then the encoded features were classified by Softmax. In the process of training, Autoencoder was trained unsupervised. Softmax was trained with supervision. Autoencoder and Softmax were stacked into a model and fine-tuned by labeled images. Experiment was conducted to compare the detection performance between HOG and HOG+Autoencoder, using images collected by vehicle-mounted infrared camera. There were 80000 images for training set and 20000 for the testing set, with a rate of 1:3 between positive and negative images. The result shows that when TPR is 95%, FPR of HOG+Autoencoder is 0.4%, while the FPR of HOG is 5% with the same TPR.

Paper Details

Date Published: 15 November 2017
PDF: 5 pages
Proc. SPIE 10605, LIDAR Imaging Detection and Target Recognition 2017, 106053Q (15 November 2017); doi: 10.1117/12.2295804
Show Author Affiliations
Tianbiao Chen, Tianjin Jinhang Technical Physics Research Institute (China)
Hao Zhang, Tianjin Jinhang Technical Physics Research Institute (China)
Wenjie Shi, Tianjin Jinhang Technical Physics Research Institute (China)
Yu Zhang, Tianjin Jinhang Technical Physics Research Institute (China)


Published in SPIE Proceedings Vol. 10605:
LIDAR Imaging Detection and Target Recognition 2017
Yueguang Lv; Weimin Bao; Weibiao Chen; Zelin Shi; Jianzhong Su; Jindong Fei; Wei Gong; Shensheng Han; Weiqi Jin; Jian Yang, Editor(s)

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