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ZIP Archive - 436.5 MB - MD5: e3ab321bd64dccb7fe4168d9a71853ab
The tutorial for DarkLABEL annotation tool, used with the project. The tool is not developed by us, we are merely providing the same version with a video tutorial for biologist to replicate the process.
ZIP Archive - 3.1 GB - MD5: 9b4d885eb752d7d569ece83ade3f5b0c
The dataset consists of a detection dataset. It is in two formats; the YOLO format and COCO format. It is given for ease of use by users. Our best-performing model is YOLOv8. Includes README.txt.
ZIP Archive - 15.8 GB - MD5: 1c3642a93d44632c29c41996dcdfdbe0
The dataset contains annotations of MOT in two formats, darklabel and MOT17 tracking challenge format. Raw videos are available with README.txt file for further details.
MPEG-4 Video - 384.5 MB - MD5: fa9cfebf43542c8aaceef043f98c857c
The example videos shows annotation plotted on raw videos to show MOT annotations provided in the dataset. The users will get 12 video sequences where all individuals in the video are tracked with use tool called dark label.
ZIP Archive - 60.5 GB - MD5: 4e901d401e670885dfab999d5811217c
Annotations for the re-identification dataset. Each annotation sequence has a pair of videos with tracking data. The tracking data is used to give IDs to individuals in videos and further, we provide ID mapping between two videos as ground truth for re-identification. This means...
MPEG-4 Video - 124.1 MB - MD5: 3390199d8151ad7def103125d14e1658
The file provides example visualization of the annotations, the videos shows combined stream from two different drones, captured simultaneously. We have marked same individual in two different streams, the annotations have information about the ID of the individual from MOT data...
ZIP Archive - 1.6 GB - MD5: 27d24ff94cdb76802cc8d5a612cc2cb1
ZIP Archive - 19.2 MB - MD5: 8136978cad8b51c6c991c4f5633bae66
ZIP Archive - 41.4 MB - MD5: e5e5b56398b062e00c839d88f1f7e6b6
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