Widely used video technology brings a large amount of video data, so it is impossible to detect abnormalities in surveillance video relying on human operators only. Automated abnormal events detection is extremely important for public safety. We propose an abnormal events detection approach with comprehensive consideration of target features and temporal-spatial context. The method exploits the texture of optical flows to describe the rigidity of moving objects. Then, we establish an abnormal events detection model of temporal context based on the hidden Markov model (HMM). Afterwards, radon features of abnormal events are extracted. We also establish the classification model of spatial context by using the HMM based on pre-classification results obtained by the support vector machine (SVM). Experimental results on public dataset—UCSD PED2 show that the performance of our method outperforms the existing algorithms in abnormal events detection and localization. Furthermore, our approach can classify abnormal events.