Crowd Behavior Recognition Using Dense Trajectories
2014 International Conference on Digital Image Computing Techniques and Applications (DICTA)（2014）
Key words
feature extraction,image classification,image coding,image representation,image sequences,motion estimation,support vector machines,L2 plus power normalization,PETS2009 S3 dataset,UMN dataset,area-under-the curve score,bag-of-words model,benchmark datasets,classification rate,crowd behavior recognition,dense optical flow field,dense points,dense-trajectories,displacement information,dynamic feature extraction,global scene representation,histogram-of-oriented gradient,locality-constrained linear encoding,median filtering,motion boundary histogram descriptors,motion trajectories,object tracking,short video sequence,sum pooling,support vector machine classifier training
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