Multi-Cattle Tracking Algorithm with Enhanced Trajectory Estimation in Precision Livestock Farms

• 영문명: Multi-Cattle Tracking Algorithm with Enhanced Trajectory Estimation in Precision Livestock Farms
• 발행기관: 한국스마트미디어학회
• 저자명: Shujie Han Alvaro Fuentes Sook Yoon Jongbin Park Dong Sun Park
• 간행물 정보: 『스마트미디어저널』Vol13, No.2, 23~31쪽, 전체 9쪽
• 주제분류: 공학 > 컴퓨터학
• 파일형태: PDF
• 발행일자: 2024.02.29

영문 초록

In precision cattle farm, reliably tracking the identity of each cattle is necessary. Effective tracking of cattle within farm environments presents a unique challenge, particularly with the need to minimize the occurrence of excessive tracking trajectories. To address this, we introduce a trajectory playback decision tree algorithm that reevaluates and cleans tracking results based on spatio-temporal relationships among trajectories. This approach considers trajectory as metadata, resulting in more realistic and accurate tracking outcomes. This algorithm showcases its robustness and capability through extensive comparisons with popular tracking models, consistently demonstrating the promotion of performance across various evaluation metrics that is HOTA, AssA, and IDF1 achieve 68.81%, 79.31%, and 84.81%.

목차

Ⅰ. INTRODUCTION

Ⅱ. MATERIALS and METHOD

Ⅲ. EXPERIMENTAL RESULTS

Ⅳ. CONCLUSION

키워드

deep learning cattle tracking decision tree post processing trajectories