253 research outputs found
Recommended from our members
Multiperson Tracking by Online Learned Grouping Model With Nonlinear Motion Context
Efficient tracking of team sport players with few game-specific annotations
One of the requirements for team sports analysis is to track and recognize
players. Many tracking and reidentification methods have been proposed in the
context of video surveillance. They show very convincing results when tested on
public datasets such as the MOT challenge. However, the performance of these
methods are not as satisfactory when applied to player tracking. Indeed, in
addition to moving very quickly and often being occluded, the players wear the
same jersey, which makes the task of reidentification very complex. Some recent
tracking methods have been developed more specifically for the team sport
context. Due to the lack of public data, these methods use private datasets
that make impossible a comparison with them. In this paper, we propose a new
generic method to track team sport players during a full game thanks to few
human annotations collected via a semi-interactive system. Non-ambiguous
tracklets and their appearance features are automatically generated with a
detection and a reidentification network both pre-trained on public datasets.
Then an incremental learning mechanism trains a Transformer to classify
identities using few game-specific human annotations. Finally, tracklets are
linked by an association algorithm. We demonstrate the efficiency of our
approach on a challenging rugby sevens dataset. To overcome the lack of public
sports tracking dataset, we publicly release this dataset at
https://kalisteo.cea.fr/index.php/free-resources/. We also show that our method
is able to track rugby sevens players during a full match, if they are
observable at a minimal resolution, with the annotation of only 6 few seconds
length tracklets per player.Comment: Accepted to 2022 8th International Workshop on Computer Vision in
Sports (CVsports 2022
Detecting, segmenting and tracking bio-medical objects
Studying the behavior patterns of biomedical objects helps scientists understand the underlying mechanisms. With computer vision techniques, automated monitoring can be implemented for efficient and effective analysis in biomedical studies. Promising applications have been carried out in various research topics, including insect group monitoring, malignant cell detection and segmentation, human organ segmentation and nano-particle tracking.
In general, applications of computer vision techniques in monitoring biomedical objects include the following stages: detection, segmentation and tracking. Challenges in each stage will potentially lead to unsatisfactory results of automated monitoring. These challenges include different foreground-background contrast, fast motion blur, clutter, object overlap and etc. In this thesis, we investigate the challenges in each stage, and we propose novel solutions with computer vision methods to overcome these challenges and help automatically monitor biomedical objects with high accuracy in different cases --Abstract, page iii
- …