This paper deals with the multi-object tracking (MOT) problem in videos acquired by 360-degree cameras. Targets are tracked by a frame-by-frame association strategy. At each frame, candidate targets are detected by a pre-trained state-of-the-art deep model. Associations to the targets known till the previous frame are found by solving a data association problem considering the locations of the targets in the scene. In case of a missing detection, a Kalman filter is used to track the target. Differently than works at the state-of-the-art, the proposed tracker considers the depth of the targets in the scene. The distance of the targets from the camera can be estimated by geometrical facts peculiar to the adopted 360-degree camera and by assuming targets move on the ground-plane. Distance estimates are used to model the location of the targets in the scene, solve the data association problem, and handle missing detection. Experimental results on publicly available data demonstrate the effectiveness of the adopted approach.
Lingua originaleEnglish
Titolo della pubblicazione ospiteImage Analysis and Processing – ICIAP 2022 21st International Conference, Lecce, Italy, May 23–27, 2022, Proceedings, Part III
Numero di pagine13
Stato di pubblicazionePublished - 2022

Serie di pubblicazioni


All Science Journal Classification (ASJC) codes

  • ???subjectarea.asjc.2600.2614???
  • ???subjectarea.asjc.1700.1700???


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