Addressing Multiple Object Tracking with Segmentation Masks
Multiple Object Tracking (MOT) aims to locate all the objects from a video, assigning them the same identities across all frames. Traditionally, this problem was addressed following the Tracking by Detection (TbD) paradigm, using detections represented by bounding boxes. However, bounding boxes can...
| Autor: | |
|---|---|
| Tipo de recurso: | tesis de maestría |
| Fecha de publicación: | 2024 |
| País: | España |
| Institución: | Universidad de Santiago de Compostela (USC) |
| Repositorio: | Minerva. Repositorio Institucional de la Universidad de Santiago de Compostela |
| Idioma: | inglés |
| OAI Identifier: | oai:minerva.usc.gal:10347/37791 |
| Acceso en línea: | https://hdl.handle.net/10347/37791 |
| Access Level: | acceso abierto |
| Palabra clave: | Multiple object tracking Segmentation Deep learning 1203 Ciencia de los ordenadores |
| Sumario: | Multiple Object Tracking (MOT) aims to locate all the objects from a video, assigning them the same identities across all frames. Traditionally, this problem was addressed following the Tracking by Detection (TbD) paradigm, using detections represented by bounding boxes. However, bounding boxes can contain information from several objects, something that does not happen with segmentation masks. This work takes the ByteTrack MOT system as a starting point. Our proposal, ByteTrackMask, integrates a class-agnostic segmentation method and a segmentation-based tracker in ByteTrack in order to rescue tracks that would have been lost. Results over validation sets of MOT challenge datasets provide improvements in MOT metrics of interest like MOTA, IDF1 and false negatives. |
|---|