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Robust person head detection based on multi-scale representation fusion of deep convolution neural network
Wang Yingying1,2; Yin Yingjie1; Wu Wenqi1,2; Sun Siyang1,2; Wang Xingang1
2017
Conference NameThe IEEE International Conference on Robotics and Biomimetics (ROBIO)
Pages296-301
Conference Date2017.12.5-2017.12.8
Conference Place澳门
Abstract
Person head detection is still a challenge due to the large variability in heads’ sizes and orientations, lighting conditions and strong occlusions. Small heads require local information contained in low level layers instead of semantic features of upper layers. But most of these fine details are lost in the early convolutional layers of the deep convolution neural networks (DCNN). In order to improve the overall detection accuracy, it is important to utilize local information from lower layers into the detection framework. In this letter, we use multi-scale representation fusion of DCNN as a way to incorporate lower layers with upper layers for detection. Our proposed model is based on the recent object detection network Single Shot MultiBox Detector (SSD). VGG16 is used as the base network. Batch normalization (BN) layers are used in our proposed multi-task learning method to accelerate training process and improve the robustness. Compared to state-of-the-art methods, our proposed detector achieves superior person head detection performance on the HollywoodHeads dataset (81.0 AP) and Casablance dataset (78.5 AP).
KeywordPerson Head Detection Deep Convolution Neural Network Multi-scale Representations Multi-task Learning
Indexed ByEI
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/19753
Collection精密感知与控制研究中心_精密感知与控制
Affiliation1.自动化研究所精密感知与控制研究中心
2.中国科学院大学
Recommended Citation
GB/T 7714
Wang Yingying,Yin Yingjie,Wu Wenqi,et al. Robust person head detection based on multi-scale representation fusion of deep convolution neural network[C],2017:296-301.
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