Video anomaly detection plays a vital role in intelligent surveillance system. We introduce an abnormal video event detection system that considers both spatial and temporal contexts. To illustrate the video, we perform the spatiotemporal paths for video event detection. Thus this new formulation can accurately detect and locate video events in cluttered and crowded scenes, and is robust to camera motions. It can also well handle the scale, shape, and intra class variations of the event. Compared to event detection using spatiotemporal sliding windows, the spatiotemporal paths correspond to the event trajectories in the video space, thus can better handle events composed by moving objects. We prove that the proposed search algorithm can achieve the global optimal solution with the lowest complexity. Experiments are conducted on realistic video data sets with different event detection tasks, such as anomaly event detection and running detection. Our proposed method is compatible with different types of video features or object detectors and robust to false and missed local detections. It significantly improves the overall detection and localization accuracy over the state-of-art methods.