YSD-BPTrack: A multi-object tracking framework for calves in occluded environments

YSD-BPTrack: A multi-object tracking framework for calves in occluded environments

Blog Article

The accurate tracking of individual calves is essential for health monitoring.However, existing multi tracking frameworks often encounter frequent ID abnormal switching issues during occlusion.To address these challenges, we propose a novel multi-object tracking framework named YSD-BPTrack for calves in occluded environments on cattle farms in this paper.This framework mainly consists of two stages: detection and tracking.Concerning the detection phase, the AEG SKK8182VZC Integrated Tower Fridge A++ DCNv4 is integrated into the YOLOv8s model to capture spatial deformation features caused by occlusion, thereby enhancing detection performance under occlusion.

Additionally, the Star operation of StarNet is also applied to the model to achieve excellent detection performance with lower computational costs.Concerning the tracking stage, we first propose an innovative rematching algorithm (Rematching module) and a new trajectory removal strategy (Trajectory removal module).The Rematching module performs rematching with detection boxes utilizing extended trajectory prediction boxes in cases of occlusion, resulting in a reduced probability of ID switch errors.Moreover, the Trajectory Removal module dynamically adjusts the removal time for lost matching trajectories, which decreases the likelihood of trajectories being mistakenly removed.Specifically, our proposed novel framework achieves a HOTA (Higher Order Tracking Accuracy) of 91.

6%, surpassing Cuticle Oil other frameworks in both track accuracy and efficiency.Experimental results also validate the superiority of the YSD-BPTrack, with HOTA increasing by 17.6%, MOTA (Multiple Object Tracking Accuracy) by 13.9%, MOTP (Multiple Object Tracking Precision) by 1.8%, IDF1 (Identification F1 Score) by 15.

4%, and reducing parameters by 49.1%, IDSw (Identification Switches) by 88.9%, and computational overhead by 39.2% compared to the other frameworks.Overall, the proposed multi-object tracking framework has great potential to revolutionize the tracking of calves.