Vehicle-counting with Automatic Region-of-Interest and Driving-Trajectory detection
This work addresses the need for automated traffic analysis, particularly for Pan-Tilt-Zoom cameras in developing countries, though it appears incremental by automating existing manual steps.
The paper tackles the problem of automating vehicle counting from traffic videos by introducing a method that automatically identifies the region of interest and driving trajectories, eliminating the need for human input. Preliminary results show an average intersection over union of 57.05% for ROI and a mean absolute error of 17.44% for vehicle counting.
Vehicle counting systems can help with vehicle analysis and traffic incident detection. Unfortunately, most existing methods require some level of human input to identify the Region of interest (ROI), movements of interest, or to establish a reference point or line to count vehicles from traffic cameras. This work introduces a method to count vehicles from traffic videos that automatically identifies the ROI for the camera, as well as the driving trajectories of the vehicles. This makes the method feasible to use with Pan-Tilt-Zoom cameras, which are frequently used in developing countries. Preliminary results indicate that the proposed method achieves an average intersection over the union of 57.05% for the ROI and a mean absolute error of just 17.44% at counting vehicles of the traffic video cameras tested.