Anomaly Detection in Traffic Scenes via Spatial-Aware Motion Reconstruction

Anomaly detection from a driver's perspective when driving is important to autonomous vehicles. As a part of Advanced Driver Assistance Systems (ADAS), it can remind the driver about dangers in a timely manner. Compared with traditional studied scenes such as a university campus and market surveillance videos, it is difficult to detect an abnormal event from a driver's perspective due to camera waggle, abidingly moving background, drastic change of vehicle velocity, etc. To tackle these specific problems, this paper proposes a spatial localization constrained sparse coding approach for anomaly detection in traffic scenes, which first measures the abnormality of motion orientation and magnitude, respectively, and then fuses these two aspects to obtain a robust detection result. The main contributions are threefold, as follows. 1) This work describes the motion orientation and magnitude of the object, respectively, in a new way, which is demonstrated to be better than the traditional motion descriptors. 2) The spatial localization of an object is taken into account considering the sparse reconstruction framework, which utilizes the scene's structural information and outperforms the conventional sparse coding methods. 3) Results of motion orientation and magnitude are adaptively weighted and fused by a Bayesian model, which makes the proposed method more robust and able to handle more kinds of abnormal events. The efficiency and effectiveness of the proposed method are validated by testing on nine difficult video sequences that we captured ourselves. Observed from the experimental results, the proposed method is more effective and efficient than the popular competitors and yields a higher performance.