Abstract
Intelligent video analysis technology has been widely used to detect moving vehicles and pedestrians in huge volume video record. In order to find a unified solution to moving vehicles and pedestrians detection in videos, BD_CNN (Background Difference_Convolutional Neural Network) algorithm is proposed in this paper, which applies background difference technology to recognize moving objects, adopts convolutional neural network algorithm to extract features of vehicle and pedestrian automatically, and builds unified vehicle and pedestrian classifier. The multi-scale detection method and reasonable decision mechanics are designed to improve the detection accuracy. Experimental results validate the algorithm’s availability and efficiency in the moving objects detection.
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Zhang, X., Fang, T., Gu, Q. (2017). BD_CNN Based Pedestrians and Vehicle Recognition in Video Surveillance. In: Yang, X., Zhai, G. (eds) Digital TV and Wireless Multimedia Communication. IFTC 2016. Communications in Computer and Information Science, vol 685. Springer, Singapore. https://doi.org/10.1007/978-981-10-4211-9_19
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DOI: https://doi.org/10.1007/978-981-10-4211-9_19
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