Event recognition of crowd video using corner optical flow and convolutional neural network

Weihan Zhang; Yibin Hou; Suyu Wang

doi:10.1117/12.2245305

29 August 2016 Event recognition of crowd video using corner optical flow and convolutional neural network

Weihan Zhang, Yibin Hou, Suyu Wang

Proceedings Volume 10033, Eighth International Conference on Digital Image Processing (ICDIP 2016); 100335K (2016) https://doi.org/10.1117/12.2245305
Event: Eighth International Conference on Digital Image Processing (ICDIP 2016), 2016, Chengu, China

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.

Citation Download Citation

Weihan Zhang, Yibin Hou, and Suyu Wang "Event recognition of crowd video using corner optical flow and convolutional neural network", Proc. SPIE 10033, Eighth International Conference on Digital Image Processing (ICDIP 2016), 100335K (29 August 2016); https://doi.org/10.1117/12.2245305

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available