Abnormal event detection using spatio-temporal feature and nonnegative locality-constrained linear coding
Yu Zhao, Lei Zhou, Keren Fu, Jie Yang · 2016
In this paper, an approach using the spatio-temporal feature and nonnegative locality-constrained linear coding (NLLC) is proposed to detect abnormal events in videos. This approach utilizes position-based spatio-temporal descriptors as the low-level representations of a video clip. Each descriptor consists of the position information of a space-time interest point and an appearance feature vector. To obtain the high-level video representations, the nonnegative locality-constrained linear coding is adopted to encode each spatio-temporal descriptor. Then, the max pooling integrates all NLLC codes of a video clip to produce a feature vector. Finally, the support vector machine (SVM) is employed to classify the feature vector as abnormal or normal. Experimental results on two datasets have demonstrated the promising performance of the proposed approach in the detection of both global and local abnormal events.