Abstract
Pedestrian detection plays an important role in intelligent analysis of images and videos. In this paper, we propose a deformation model based convolutional neural network(D-CNN) for pedestrian detection. Enlightened by YOLO model, D-CNN network integrates deformation and occlusion handling into the network to improve the accuracy of occluded pedestrian detection. The performance of D-CNN is evaluated on two popular datasets as well as pictures got in daily life. Among the state-of-the-art methods compared in this paper, the comprehensive performance of D-CNN is the best, whose mAP is only 0.4 points lower than the highest one but the detection speed doubles. So our proposed network can get real-time speed while maintaining rather satisfying precision of pedestrian detection.
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Acknowledgment
This work was supported by National Natural Science Foundation of China (Grant Nos. 81101119 and 61672231).
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Gao, Z., Chen, W., Cao, G., Chen, P. (2018). Pedestrian Detection with D-CNN. In: Bi, Y., Chen, G., Deng, Q., Wang, Y. (eds) Embedded Systems Technology. ESTC 2017. Communications in Computer and Information Science, vol 857. Springer, Singapore. https://doi.org/10.1007/978-981-13-1026-3_13
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DOI: https://doi.org/10.1007/978-981-13-1026-3_13
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