MOT17

doi:doi:10.57702/qhmpqsss

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 algorithms, such as the Kalman Filter, often work well in linear motion scenarios but struggle to accurately predict the locations of objects undergoing complex and non-linear movements.

BibTex:

@dataset{Siddharth_Sagar_Nijhawan_and_Leo_Hoshikawa_and_Atsushi_Irie_and_Masakazu_Yoshimura_and_Junji_Otsuka_and_Takeshi_Ohashi_2024,
    abstract = {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 algorithms, such as the Kalman Filter, often work well in linear motion scenarios but struggle to accurately predict the locations of objects undergoing complex and non-linear movements.},
    author = {Siddharth Sagar Nijhawan and Leo Hoshikawa and Atsushi Irie and Masakazu Yoshimura and Junji Otsuka and Takeshi Ohashi},
    doi = {10.57702/qhmpqsss},
    institution = {No Organization},
    keyword = {'CrowdHuman', 'Kalman Filter', 'MOT17', 'MOT17 half-half', 'Multi-Object Tracking', 'Multiple Object Tracking', 'Object Detection', 'People Tracking', 'Tracking', 'car tracking', 'complex scenarios', 'motion information', 'multi-object tracking', 'non-linear movements', 'pedestrian tracking'},
    month = {dec},
    publisher = {TIB},
    title = {MOT17},
    url = {https://service.tib.eu/ldmservice/dataset/mot17},
    year = {2024}
}