18 datasets found

Tags: multi-object tracking

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  • MOT Challenge

    Object detectors are vital to many modern computer vision applications. However, even state-of-the-art object detectors exhibit inconsistent behavior when the input undergoes...
  • DanceTrack

    Many Multi-Object Tracking (MOT) approaches exploit motion information to associate all the detected objects across frames. However, many methods that rely on filtering-based...
  • MOT20

    The dataset used for the MOT20 challenge, which consists of 8 different sequences depicting very crowded challenging scenes.
  • BDD100K MOTS

    The BDD100K MOTS dataset is a subset of the BDD100K dataset, containing 154 videos with annotation for training and validation, and 37 videos for testing.
  • MOTS Dataset

    The MOTS dataset is a variant of the MOT dataset, containing 4 training sequences and 4 test sequences.
  • MOT16 and MOT17 Datasets

    The MOT16 and MOT17 datasets are used for evaluating the performance of multi-object tracking algorithms.
  • PETS-09

    Multi-Camera Multi-Object Tracking (MC-MOT) dataset for reconfigurable spatial-temporal graph model
  • CAMPUS

    Multi-Camera Multi-Object Tracking (MC-MOT) dataset for reconfigurable spatial-temporal graph model
  • Wildtrack

    Multi-Camera Multi-Object Tracking (MC-MOT) dataset for reconfigurable spatial-temporal graph model
  • H3D Dataset

    The H3D dataset for full-surround 3D multi-object detection and tracking in crowded urban scenes.
  • MOT20: A Benchmark for Multi-Object Tracking

    MOT-Challenge datasets are a benchmark for multi-object tracking.
  • MOT16: A Benchmark for Multi-Object Tracking

    MOT-Challenge datasets are a benchmark for multi-object tracking.
  • MOT-Challenge datasets

    MOT-Challenge datasets are a benchmark for multi-object tracking.
  • Tracklet-Switch Adversarial Attack against Pedestrian Multi-Object Tracking T...

    Multi-Object Tracking (MOT) has achieved aggressive progress and derived many excellent deep learning trackers. Meanwhile, most deep learning models are known to be vulnerable...
  • APOLLO MOTS

    More challenging MOTS dataset with higher instance density and crowded scenes.
  • KITTI MOTS

    Multi-object tracking and segmentation (MOTS) dataset, with dense instance segment annotations.
  • MOT17

    Many Multi-Object Tracking (MOT) approaches exploit motion information to associate all the detected objects across frames. However, many methods that rely on filtering-based...
  • KITTI dataset

    The dataset used in the paper is the KITTI dataset, which is a benchmark for monocular depth estimation. The dataset consists of a large collection of images and corresponding...