MOT20

MOT20 MOT20 is a benchmark dataset for single-camera multi-object tracking (MOT) and pedestrian detection in very crowded real-world scenes. This Hugging Face repository provides MOT20 in the original MOTChallenge-style structure for research, benchmarking, training, and evaluation of multi-object tracking systems. MOT20 was introduced to stress-test MOT methods in high-density pedestrian scenes, including crowded squares, indoor train stations, stadium exits, and pedestrian… See the full description on the dataset page: https://huggingface.co/datasets/Lekim89/MOT20.

Papers using MOT20 (9)

TransTrack: Multiple Object Tracking with Transformer2020 · 359 citesProbabilistic Tracklet Scoring And Inpainting For Multiple Object Tracking2020 · 85 citesDeep OC-SORT: Multi-Pedestrian Tracking by Adaptive Re-Identification2023 · 10 citesSimple Cues Lead To A Strong Multi-object Tracker2022 · 6 citesTracking Objects as Pixel-wise Distributions2022 · 1 citesLITE: A Paradigm Shift In Multi-object Tracking With Efficient Reid Feature Integration2024 · 1 citesDeNoising-MOT: Towards Multiple Object Tracking with Severe Occlusions2023When To Extract Reid Features: A Selective Approach For Improved Multiple Object Tracking2024Localization-based Tracking2021