Squirrel-Cage Local Binary Pattern and Its Application in Video Anomaly Detection

Abstract

Local binary pattern (LBP) is one of the most successful feature descriptors. However, LBP and its variants have not been as successful as other feature descriptors in video anomaly detection (VAD). This is because LBP and its variants are mainly designed for spatial texture analysis. Although the volume LBP (VLBP) and the LBP-three orthogonal planes (LPB-TOP) have the capability of describing dynamic texture, they are seldom used as descriptors for VAD because 1) both VLBP and LBP-TOP are more suitable for natural scenes with rich dynamic textures, but sensitive to noise in the scenes with less dynamic textures, 2) the combination of motion and appearance not only limits their capability of motion characterizing but also brings the irrelevant appearance information such as background, and 3) high dimensionality is another drawback. In this paper, a new variant of the LBP called the squirrel-cage LBP (SCLBP) is proposed for VAD. By imitating the structure of squirrel cage rotor, the proposed SCLBP can be regarded as a stretched LBP in temporal direction and has two distinct features: 1) it is computed at vector-wise, rather than at pixel-wise (i.e., for the central vector with respect to its surrounding parallel vectors), and 2) the sign between two vectors is determined by the angle-based thresholding scheme. The SCLBP can effectively encode the motion information and is insensitive to noise and irrelevant disturbances caused by dynamic background and illumination change. To the best of our knowledge, The SCLBP is the first variant of the LBP specially designed for motion characterizing. The SCLBP has great flexibility, extendibility, and low dimensionality (only one-third of the LBP-TOP descriptor). The effectiveness of the proposed SCLBP descriptor is demonstrated on different public data sets and compared with the other dominant descriptors and state-of-the-art approaches in VAD.