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

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 realtime 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.

[1]  Qiang Ji,et al.  Video event recognition with deep hierarchical context model , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[2]  Jonathan Tompson,et al.  Efficient ConvNet-based marker-less motion capture in general scenes with a low number of cameras , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[3]  Hichem SNOUSSI,et al.  Detection of Visual Abnormal Events via One-class SVM , 2012 .

[4]  Trevor Darrell,et al.  Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation , 2013, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[5]  Martin D. Levine,et al.  An on-line, real-time learning method for detecting anomalies in videos using spatio-temporal compositions , 2013, Comput. Vis. Image Underst..

[6]  Hyeran Byun,et al.  Motion pattern analysis using partial trajectories for abnormal movement detection in crowded scenes , 2013 .

[7]  Bingbing Ni,et al.  Motion Part Regularization: Improving action recognition via trajectory group selection , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[8]  Simone Calderara,et al.  Visual Tracking: An Experimental Survey , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  Trevor Darrell,et al.  Long-term recurrent convolutional networks for visual recognition and description , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[10]  Deva Ramanan,et al.  Parsing Videos of Actions with Segmental Grammars , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[11]  Afshin Dehghan,et al.  Part-based multiple-person tracking with partial occlusion handling , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.