NDLI: Real-time target detection and recognition with deep convolutional networks for intelligent visual surveillance

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Real-time target detection and recognition with deep convolutional networks for intelligent visual surveillance

Content Provider	ACM Digital Library
Author	Xu, Wen Mao, Bo Cao, Jie Zhang, Hao Lan He, Jing
Abstract	Moving target detection and tracking, recognition, behaviors analysis are the key issues in the intelligent visual surveillance system (IVSS). The challenge is how to process the real-time video stream in an effective way in case that we could find the interested objects for analysis. However, the traditional video surveillance technology often does not meet the needs of real-time key frame recognition for the on-line intelligent video monitoring system. In our paper, we adopt the state-of-the-art Faster R-CNN [7] that takes advantages of convolutional neural networks into our real-time target recognition system - Deep Intelligent Visual Surveillance (DIVS). The key aspects of our DIVS are consisted of four parts: (i) Getting the real-time video images from remote cameras; (ii) Processing the data with the deep learning framework caffe [23] built for Faster R-CNN; (iii) Storing the valuable data with MySQL; (iv) Data presentation on the website. Experiments based on our system validated the effectiveness, stability and accuracy of our proposed solutions.
Starting Page	321
Ending Page	326
Page Count	6
File Format	PDF
ISBN	9781450346160
DOI	10.1145/2996890.3007881
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2016-12-06
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Intelligent visual surveillance Target recognition Real-time Caffe Convolutional neural networks Faster r-cnn
Content Type	Text
Resource Type	Article

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