Dataset Comparison
- Shrinjay Mukherjee
- Jinghao Lei
UAV Human
Download Link: https://drive.google.com/drive/folders/1QeYXeM_pbWBSSmpRr_rKHurMpI2TxAKs
A dataset containing a wider variety of traffic scenarios than the Stanford set. The angle of the camera is more of a bird's eye and a bigger focus on human behaviour rather than just the detection of a person. Similar to the Stanford set, it is also videos and is 67,428 videos long.
The videos appeared to be unlabelled however, the repo can convert the videos to frames and then each frame we will draw bounding boxes and then add Yolo format labels
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Stanford
Page: https://cvgl.stanford.edu/projects/uav_data/ (Download is on the site) (69GB zip file of videos)
A dataset containing videos of traffic. Each separate entity is labelled with colored boxes
The dataset labels bicyclists, pedestrians, skateboarders, carts, cars and buses. (Honestly if our goal is to detect people, it might not be very helpful to have all those different types of traffic as well. The angle of the video is top down
Pedestrians and objects are labelled with colored bounding boxes however there is no pixel location labelling for entities
This repo GitHub - rockkingjy/DataFormat_sdd2kitti: Convert the data from stanford drone dataset to kitti format is able to convert the videos to images and then make position of bounding boxes to a text file in KITTI format
This repo DLBD/scripts/convert-kitti-to-yolo.py at master ยท CUFCTL/DLBD can then convert KITTI format to YOLO format
UCF-ARG
โฆ details go here
Possible other datasets
โฆdetails go here