  Anomaly detection through video analysis is of great importance to detect any
anomalous vehicle/human behavior at a traffic intersection. While most existing
works use neural networks and conventional machine learning methods based on
provided dataset, we will use object recognition (Faster R-CNN) to identify
objects labels and their corresponding location in the video scene as the first
step to implement anomaly detection. Then, the optical flow will be utilized to
identify adaptive traffic flows in each region of the frame. Basically, we
propose an alternative method for unusual activity detection using an adaptive
anomaly detection framework. Compared to the baseline method described in the
reference paper, our method is more efficient and yields the comparable
accuracy.
