Violent video detection based on MoGLOH feature

Wu Wang; Yunfei Cheng; Lijuan Hong; Wen Wang

doi:10.1117/12.2283062

24 October 2017 Violent video detection based on MoGLOH feature

Wu Wang, Yunfei Cheng, Lijuan Hong, Wen Wang

Proceedings Volume 10462, AOPC 2017: Optical Sensing and Imaging Technology and Applications; 1046210 (2017) https://doi.org/10.1117/12.2283062
Event: Applied Optics and Photonics China (AOPC2017), 2017, Beijing, China

Abstract

Violent detection from video is a hot topic which has wide application. The aim of this paper is to design a novel feature descriptors called motion gradient location and orientation histogram (MoGLOH), which encode not only the local appearance but also explicitly models local motion. Our proposed MoGLOH is composed of two part of information. The first part is the gradient location and orientation histogram (GLOH) describing the spatial appearance, and the second part is an aggregated histogram of optical flow with a log-polar location grid named Optical Flow Orientation Histogram (OFOH) which indicate the movement of feature point. To eliminate the feature noise, the non-parametric Kernel Density Estimation (KDE) is employed on the MoGLOH descriptor. The theoretical analysis demonstrates the proposed algorithm performs robustly and favorably.

Citation Download Citation

Wu Wang, Yunfei Cheng, Lijuan Hong, and Wen Wang "Violent video detection based on MoGLOH feature", Proc. SPIE 10462, AOPC 2017: Optical Sensing and Imaging Technology and Applications, 1046210 (24 October 2017); https://doi.org/10.1117/12.2283062

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