BMCTrack-d is a novel tracking-by-detection method that uses unique back marks on pigs to achieve robust re-identification and tracking in challenging side-view camera settings. The approach employs a neural network-based back mark classifier followed by temporal consistency checks and deduplication to improve identity reliability over time. On a demanding test set, BMCTrack-d outperforms strong baselines, achieving higher-order tracking accuracy gains of 9.11% and 1.03%.
By David Brunner, Maciej Oczak, Marie Bordes, Jean-Loup Rault, Stephan M. Winkler, Viktoria Dorfer
arXiv:2609.17427v1 Announce Type: cross
Abstract: Real-time multi-object tracking systems remain highly vulnerable to full and long-term occlusion, where targets temporarily or completely disappear f...
By Mais Mohammed, Sharifa Mohammed, Hanan Awadh, Haneen Bamaas, Raghad Bawazeer, Elham Alghamdi
arXiv:2607. 17157v1 Announce Type: cross Abstract: Multi-object tracking (MOT) aims to localize multiple objects in videos while preserving their identities over time.
By Yanrong Qin, Xiaoyan Cao, Yao Yao
PuTR-CouT is a transformer‑based counting‑by‑tracking framework designed for camera‑trap image sequences. It generates synthetic training data using structural priors to create pseudo‑tracking labels, enabling the tracker to associate detections across frames and estimate per‑species counts. The method improves upon the MaxBoxCount baseline on the iWildCam 2021 benchmark, offering competitive counting results along with multi‑species predictions and track‑level verification.
By Fagner Cunha, Juan G. Colonna, Eulanda M. dos Santos
arXiv:2606. 24986v1 Announce Type: new Abstract: Automated cattle posture-classification systems frequently report near-perfect accuracy, yet their robustness under realistic deployment conditions remains largely unknown.
By Leutrim Uka, Severino Pinto, Gundula Hoffmann, Marina M. -C. H\"ohne
arXiv:2602. 14771v5 Announce Type: replace-cross Abstract: The human visual system tracks objects by integrating current observations with previously observed information, adapting to target and scene changes, and reasoning about occlusion at fine granularity.
By Shih-Fang Chen, Jun-Cheng Chen, I-Hong Jhuo, Yen-Yu Lin