Low-rank structured sparse representation and reduced dictionary learning-based abnormity detection

A novel abnormity detection method is presented which combines the low-rank structured sparse representation and reduced dictionary learning. The multi-scale three-dimensional gradient is used as low-level feature by encoding the spatiotemporal information. A group of reduced sparse dictionaries is learnt by low-rank approximation based on the structured sparsity property of the video sequence. The contribution of this study is three-fold: (i) the normal feature clusters can be represented effectively by the reduced dictionaries which are learnt based on the low-rank nature of the data; (ii) the size of dictionary is determined adaptively by the sparse learning method according to the scene, which makes the representation more compact and efficient; and (iii) the proposed abnormity detection method is of low time complexity and real-time detection can be obtained. The authors have evaluated the proposed method against the state-of-the-art methods on the public datasets and very promising results have been achieved.