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Proceedings Paper

Event recognition of crowd video using corner optical flow and convolutional neural network
Author(s): Weihan Zhang; Yibin Hou; Suyu Wang
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Paper Abstract

Event recognition is the process of determining the event type and state of crowd on video under analysis by a machine learning process. In order to improve the accuracy, this paper proposes a method that using optical flow of corner points and convolutional neural network to recognize crowd events on video. First, extract and filter the FAST (Features from Accelerated Segment Test) corner points. Then, track those points using Lucas-Kanade optical flow and get coordinate vectors. Finally, train an improved convolutional neural network based on LeNet model. Experiment on the PETS 2009 dataset using surveillance systems shows that, Average error rate for classifying the 6 crowd events is 0.11. So the method can recognize a variety of defined crowd events and improve the accuracy of recognition.

Paper Details

Date Published: 29 August 2016
PDF: 5 pages
Proc. SPIE 10033, Eighth International Conference on Digital Image Processing (ICDIP 2016), 100335K (29 August 2016); doi: 10.1117/12.2245305
Show Author Affiliations
Weihan Zhang, Beijing Univ. of Technology (China)
Yibin Hou, Beijing Univ. of Technology (China)
Suyu Wang, Beijing Univ. of Technology (China)


Published in SPIE Proceedings Vol. 10033:
Eighth International Conference on Digital Image Processing (ICDIP 2016)
Charles M. Falco; Xudong Jiang, Editor(s)

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