Abstract
A group activities recognition algorithm is proposed by combining M-DTCWT (multi directional dual tree complex wavelet transform) with elliptic Mahalanobis metric. M-DTCWT is composed of directional filter bank in cascade with dual tree complex wavelet transform. By using the M-DTCWT to decompose the human images in videos for multi-scale and multi-direction, the high and low frequency coefficients can be obtained. The texture features of the high and low frequency coefficients are extracted by using improved local binary pattern and gray level co-occurrence matrix and classified by using elliptic Mahalanobis metric. According to the results of classification, group activities are recognized. Experimental results on Group activity video set and self built video set show that the proposed algorithm has higher recognition accuracy than the existing algorithms and Euclidean metric algorithm.
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Acknowledgements
This work has been partially supported by the National Natural Science Foundation of China (61672032, 61401001), the Natural Science Foundation of Anhui Province under Grant 1408085MF121, and the Opening Foundation of polarization imaging detection technology of Anhui Key Laboratory (2016-KFKT-003).
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Hu, G., Li, M., Liang, D., Bao, W. (2018). Recognition of Group Activities Based on M-DTCWT and Elliptic Mahalanobis Metrics. In: Wang, Y., et al. Advances in Image and Graphics Technologies. IGTA 2017. Communications in Computer and Information Science, vol 757. Springer, Singapore. https://doi.org/10.1007/978-981-10-7389-2_12
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DOI: https://doi.org/10.1007/978-981-10-7389-2_12
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