Practical methods for sparsity based video anomaly detection
Video anomaly detection can be used in the transportation domain to identify unusual patterns such as traffic violations, accidents, unsafe driver behavior, street crime, and other suspicious activities. Recently, sparse reconstruction techniques have been used for image classification, and shown to provide excellent robustness to occlusion. This progress has also been leveraged for sparsity based video anomaly detection where test trajectories are expressed as sparse linear combinations of example trajectories from a given (normal or anomalous) class. While sparsity based anomaly detection techniques are promising, they pose practical challenges due to their increased computational burden and the need for generous manually labeled training (even if only for normal event trajectories). Our work focuses on overcoming these limitations. Our central contribution is a dictionary design and optimization technique that can effectively reduce the size of training dictionaries that enable sparsity based classification/anomaly detection without adversely influencing detection performance. We also suggest the use of state of the art automatic trajectory clustering techniques for initializing dictionaries which can alleviate the burden of manual labeling. Experimental results show that significant computational advantages can be obtained with the proposed techniques with little performance loss over using large and manually labeled dictionaries of example trajectories.