• Install dependencies using conda from environment.yml
• Create autonomous-traffic-monitoring/external/ folder
• Install Detectron2 in autonomous-traffic-monitoring/external/
• Download and add Mask R-CNN model weights (R101-FPN) from Detectron2 Model Zoo in autonomous-traffic-monitoring/detection/detetctron2-model-weights
• Clone DeepSORT repository and put it in autonomous-traffic-monitoring/external/
• Clone IOU/VIOU Tracker repository and put it in autonomous-traffic-monitoring/external/
• Download and add DeepSORT model weights in autonomous-traffic-monitoring/tracking/deep-sort-model-weights/
• [OPTIONAL (alternative camera calibration)] Install monodepth2 in and put model weights (mono+stereo_640x192) in autonomous-traffic-monitoring/camera/models/

• auto-traffic-monitoring/input/scripts/get_tfl_videos.py
• auto-traffic-monitoring/input/scripts/get_frames_from_video.py video.mp4

Get landmarks from location of camera in Google Maps and match points with frame. Format .csv file as u, v, X, Y, Y

• Main method (recommended): auto-traffic-monitoring/camera/run_camera.py --video_path input/videos/video.mp4 --landmarks_path input/landmarks/video_landmarks.csv --mode manual
• Alternative method: auto-traffic-monitoring/camera/run_camera.py --video_path input/videos/video.mp4 --landmarks_path input/landmarks/video_landmarks.csv --mode manual

auto-traffic-monitoring/detection/run_detection.py --video_path input/videos/video.mp4 --camera_params results/video.mp4_camera_parameters.pickle

auto-traffic-monitoring/tracking/run_tracking.py --video_path input/videos/video.mp4 --detection results/video.mp4_detection.csv --camera_params results/video.mp4_camera_parameters.pickle

auto-traffic-monitoring/tracking/run_speed_estimation.py --video_path input/videos/video.mp4 --tracking results/video.mp4_tracking_iou.csv --camera_params results/video.mp4_camera_parameters.pickle