DroneTrafficZA2020: A sample dataset of N4 freeway traffic adjacent to Engineering 4.0 in South Africa

Published: 17-08-2020| Version 1 | DOI: 10.17632/wdfkxsnpzc.1
André Broekman,
Petrus J. Gräbe,
Wynand Steyn


Dataset associated with the primary article: Real-time traffic quantization using a mini edge artificial intelligence platform A video sample captured by a DJI Mavic Air on the 28th of July at 11:30 of the N4 freeway located in Pretoria, South Africa, adjacent to the Engineering 4.0 campus. The high-definition video (4K, 29.97fps) is 18 minutes and 45 seconds in length (2020718_Mavic_Air_4K.mp4). The second video is a cropped version (1220x560 px) used for the analysis (20200728_Mavir_Air_Cropped.mp4). The analysis involves: (1) generating a ground truth dataset of every vehicle (either a "car" or "truck") entering the frame; only vehicles travelling West (moving right-to-left) toward Hatfield is considered. The dataset contains both the frame number the vehicle is first observed and its associated class, and (2) automatic object detection and counting of vehicles as processed by OpenDataCam. Both the ground truth (groundTruth.csv) and inference (OpenDataCam.csv) dataset is provided in a CSV file format. The line counter configuration file from OpenDataCam (line_counter_config.json) is provided to aid reproducibility. The Python script (ProcessOpenDataCam.py) to process the data is also provided. OpenDataCam, with the correct threshold settings of the AI network, manages to count vehicles to within a 5% accurarcy. Summary of files: + 2020718_Mavic_Air_4K.mp4 - 4K resolution video file captured by the drone + 20200728_Mavir_Air_Cropped.mp4 - Cropped 4K video centered about the freeway (1220x560 px) + groundTruth.csv - ground truth data of binary vehicle classification + OpenDataCam.csv - vehicle counting and detection output data provided by OpenDataCam + line_counter_config.json - line counter configuration file from OpenDataCam + processOpenDataCam.py - Python script file that processes the CSV files


Steps to reproduce

1) Download and install OpenDataCam according to the GitHub instructions. 2) Launch OpenDataCam. 3) Load the line counter configuration file provided. 4) Update the OpenDataCam configuration file to use the cropped video file provided. 5) Process the video file using OpenDataCam. 6) Export the CSV data file from OpenDataCam. 7) Place both OpenDataCam's and the ground truth data file in the same directory. 8) Run the Python processing script to generate the statistics and graphs. 9) Compare the difference between the OpenDataCam results and ground truth data to determine the inference accuracy.