  Due to its relevance in intelligent transportation systems, anomaly detection
in traffic videos has recently received much interest. It remains a difficult
problem due to a variety of factors influencing the video quality of a
real-time traffic feed, such as temperature, perspective, lighting conditions,
and so on. Even though state-of-the-art methods perform well on the available
benchmark datasets, they need a large amount of external training data as well
as substantial computational resources. In this paper, we propose an efficient
approach for a video anomaly detection system which is capable of running at
the edge devices, e.g., on a roadside camera. The proposed approach comprises a
pre-processing module that detects changes in the scene and removes the
corrupted frames, a two-stage background modelling module and a two-stage
object detector. Finally, a backtracking anomaly detection algorithm computes a
similarity statistic and decides on the onset time of the anomaly. We also
propose a sequential change detection algorithm that can quickly adapt to a new
scene and detect changes in the similarity statistic. Experimental results on
the Track 4 test set of the 2021 AI City Challenge show the efficacy of the
proposed framework as we achieve an F1-score of 0.9157 along with 8.4027 root
mean square error (RMSE) and are ranked fourth in the competition.
