Recognition of Group Activities Using Complex Wavelet Domain Based Cayley-Klein Metric Learning
Recognition of Group Activities Using Complex Wavelet Domain Based Cayley-Klein Metric Learning

A group activity recognition algorithm is proposed to improve the recognition accuracy in video surveillance by using complex wavelet domain based Cayley-Klein metric learning. Non-sampled dual-tree complex wavelet packet transform (NS-DTCWPT) is used to decompose the human images in videos into multi-scale and multi-resolution. An improved local binary pattern (ILBP) and an inner-distance shape context (IDSC) combined with bag-of-words model is adopted to extract the decomposed high and low frequency coefficient features. The extracted coefficient features of the training samples are used to optimize Cayley-Klein metric matrix by solving a nonlinear optimization problem. The group activities in videos are recognized by using the method of feature extraction and Cayley-Klein metric learning. Experimental results on behave video set, group activity video set, and self-built video set show that the proposed algorithm has higher recognition accuracy than the existing algorithms.
A group activity recognition algorithm is proposed to improve the recognition accuracy in video surveillance by using complex wavelet domain based Cayley-Klein metric learning. Non-sampled dual-tree complex wavelet packet transform (NS-DTCWPT) is used to decompose the human images in videos into multi-scale and multi-resolution. An improved local binary pattern (ILBP) and an inner-distance shape context (IDSC) combined with bag-of-words model is adopted to extract the decomposed high and low frequency coefficient features. The extracted coefficient features of the training samples are used to optimize Cayley-Klein metric matrix by solving a nonlinear optimization problem. The group activities in videos are recognized by using the method of feature extraction and Cayley-Klein metric learning. Experimental results on behave video set, group activity video set, and self-built video set show that the proposed algorithm has higher recognition accuracy than the existing algorithms.