Spatio-temporal descriptor for abnormal human activity detection

There has been an increased interest in the field of abnormal human activity detection to find a good descriptor with a lower computational cost. In this paper, we propose such a Spatio-Temporal Descriptor (STD) based on spatio-temporal features of an image sequence. Proposed descriptor is based on a texture map, known as Spatio-Temporal Texture Map (STTM) and is based on 3-dimensional Harris function. It is able to capture subtle variations in the spatio-temporal domain. Performance of the STD was illustrated with a mixture of Gaussian Hidden Markov Model (HMM) to show its potential for more complex modeling. Proposed algorithm was evaluated with UCSD dataset that has abnormal events that are not staged such as biker, skater, cart activities etc. Compared to other state of the art descriptors that are used with the same dataset, our proposed descriptor shows competitive performance with a lower computational cost.