Occlusion object detection via collaborative sensing deep convolution network

doi:10.1109/ACPR.2017.90

	Occlusion object detection via collaborative sensing deep convolution network
	Li, Ce; Zhao, Xinyu; Liu, Hao; Xiao, Limei
	2018-12-13
会议日期	November 26, 2017 - November 29, 2017
会议地点	Nanjing, China
关键词	Chemical detection Convolution Feature extraction Object recognition CO detection Collaborative detection Collaborative sensing collaboretive sensing Joint detection Missing features Precise positioning State-of-the-art methods
DOI	10.1109/ACPR.2017.90
页码	202-207
英文摘要	Object detection is one of the important problems in computer vision. But external occlusion often cause object features missing which lead to a big challenge of object detection. Aim at the problem of occlusion object detection and try to describe object features more effectively; we proposed a collaborative sensing deep convolution network to achieve co-detection by global and partial features of objects. Firstly, we divide the global and partial of the object, it means we segment parent and child in an object. Then, the joint detection network of parent and child is constructed. Finally, through the collaborative detection we achieve the precise positioning and recognition about parents. The proposed algorithm effectively solves the problem that object can not be detected due to missing features. We also ensure the accuracy of parent construction by child. Experiment results demonstrate that our algorithm performs better than other state-of-The-Art methods. © 2017 IEEE.
会议录	Proceedings - 4th Asian Conference on Pattern Recognition, ACPR 2017
会议录出版者	Institute of Electrical and Electronics Engineers Inc., United States
语种	英语
内容类型	会议论文
源URL	[http://ir.lut.edu.cn/handle/2XXMBERH/118077]
专题	电气工程与信息工程学院
作者单位	College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou; 730050, China
推荐引用方式 GB/T 7714	Li, Ce,Zhao, Xinyu,Liu, Hao,et al. Occlusion object detection via collaborative sensing deep convolution network[C]. 见:. Nanjing, China. November 26, 2017 - November 29, 2017.