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Group Behavior Recognition in Videos based on Cam-Shift Tracking and Histogram Changing Rate

Volume 14, Number 7, July 2018, pp. 1600-1608
DOI: 10.23940/ijpe.18.07.p24.16001608

Shuang Liua, Peng Chenb, Yanli Yua, Xing Cuia, and Denis Špeličc

aSchool of Computer Science & Engineering, Dalian Minzu University, Dalian, 116605, China
bDepartment of Software Engineering, Dalian Neusoft University of Information, Dalian, 116030, China
cFaculty of Electrical Engineering and Computer Science, University of Maribor, Maribor, SI-2000, Slovenia

(Submitted on March 11, 2018; Revised on April 24, 2018; Accepted on June 6, 2018)

Abstract:

With more and more cameras installed in public places, video surveillance systems play an increasingly important role in public safety. Research on intelligent video monitoring, especially activity recognition, is attracting increasing attention in the field of image processing. Unlike activity recognition of a single tracking object, group activity is more complex and difficult to recognize. To design a fast real-time group activity recognition algorithm without other auxiliary data, low computational cost is our focus. There are four steps for our group activity recognition system: preprocessing the captured videos, extracting foregrounds from backgrounds, tracking multiple objects and recognizing group activity. To remove noise in each frame image, the combination of the Gaussian filter algorithm and median filter algorithm is used in the preprocessing step. Then, the Gaussian mixture model is adopted to extract the foreground image. To ensure low computational cost, real-time Cam-Shift is chosen to track group activity with morphological operations in the tracking step. In the recognition step, the changing histogram rate is defined as the measure of identifying group behavior. Here, the changing histogram rate refers to the number of changing histograms and changing proportions. Experimental results show that the group activity recognition algorithm proposed in this paper is effective with low computational cost.

 

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