abnormal event detection is extremely important, especially for video surveillance. nowadays, many detectors have been proposed based on hand-crafted features. however, it remains challenging to effectively distinguish abnormal events from normal ones. this paper proposes a deep representation based algorithm which extracts features in an unsupervised fashion. specially, appearance, texture, and short-term motion features are automatically learned and fused with stacked denoising autoencoders. subsequently, long-term temporal clues are modeled with a long short-term memory (lstm) recurrent network, in order to discover meaningful regularities of video events. the abnormal events are identified as samples which disobey these regularities. moreover, this paper proposes a spatial anomaly detection strategy via manifold ranking, aiming at excluding false alarms. experiments and comparisons on real world datasets show that the proposed algorithm outper-forms state of the arts for the abnormal event detection problem in crowded scenes. © 2016 acm.