WCSE

Vehicle Detection from Static Images in Unrestricted Scenes Using Deep Convolutional Neural Network

Abstract— Most of the traditional methods, which extract manual feature from data, are based on the particular scene or video source. In this paper, we propose a vehicle detection method that targets to the static images in unrestricted scenes. Firstly, we measure similarities of all initialization regions and merge them by some rules to get bounding boxes. Then the features of these bounding boxes are extracted by deep convolutional neural network (D-CNN) respectively. Finally, Lib-SVM classifier is employed to classify each bounding box and to complete vehicle detection. Compared with traditional method, the proposed strategy performs stronger robustness.

Index Terms— vehicle detection, similarity measure, static images, deep convolutional neural network

Zhuo Yan, Cheng Cheng, Yi Xie, Jianting Fu, Yu Shi, Xiangdong Zhou, Jiahu Yuan
Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, CHINA
Zhuo Yan, Jianting Fu, Peng Cheng
University of Chinese Academy of Sciences, CHINA
Xiangdong Zhou
Automated Reasoning and Cognition Key Laboratory of Chongqing, CHINA

[Download]

Cite: Zhuo Yan, Cheng Cheng, Yi Xie, Jianting Fu, Peng Cheng, Yu Shi, Xiangdong Zhou, Jiahu Yuan, "Vehicle Detection from Static Images in Unrestricted Scenes Using Deep Convolutional Neural Network," Proceedings of 2017 the 7th International Workshop on Computer Science and Engineering, pp. 267-271, Beijing, 25-27 June, 2017.