MOT-20
Emerging8papers using it
2020first seen
MOT-20 is a benchmark dataset used to evaluate multiple object tracking methods, containing sequences of video data with annotated object identities to assess tracking performance.
Papers using MOT-20 (8)
- StableTrack: Stabilizing Multi-Object Tracking on Low-Frequency DetectionsOpen-World Object Counting in VideosFusionSORT: Fusion Methods for Online Multi-object Visual TrackingYOLO11-JDE: Fast and Accurate Multi-Object Tracking with Self-Supervised Re-IDTransTrack: Multiple Object Tracking with TransformerDeep OC-SORT: Multi-Pedestrian Tracking by Adaptive Re-IdentificationDeNoising-MOT: Towards Multiple Object Tracking with Severe OcclusionsTracking Objects as Pixel-wise Distributions