BMCTrack-d is a novel tracking‑by‑detection system that uses unique back marks on pigs to enable reliable re‑identification and tracking in challenging side‑view camera settings. The approach combines a neural network back‑mark classifier with two post‑processing stages—temporal prediction consistency checks and deduplication—to improve identity reliability over time. On a demanding test set, BMCTrack‑d outperforms strong baselines (BoT‑SORT‑ReID and TrackTrack‑ReID) by 9.11% and 1.03% in higher‑order tracking accuracy, demonstrating the effectiveness of back‑mark‑based re‑identification for individual‑level pig monitoring.
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
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
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:2512.07776v2 Announce Type: replace
Abstract: Monitoring critically endangered western lowland gorillas is currently hampered by the immense manual effort required to re-identify individuals fr...
By Maximilian Schall, Felix Leonard Kn\"ofel, Noah Elias K\"onig, Jan Jonas Kubeler, Maximilian von Klinski, Joan Wilhelm Linnemann, Xiaoshi Liu, Iven Jelle Schlegelmilch, Ole Woyciniuk, Alexandra Schild, Dante Wasmuht, Magdalena Bermejo Espinet, German Illera Basas, Gerard de Melo
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
GRACE is a camera‑efficient multi‑view pedestrian tracker that reduces the number of required cameras while maintaining high tracking accuracy. It combines volumetric‑guided fusion of homography‑based BEV features with 3D‑lifted features, uses ray conditioning to incorporate each camera’s viewing direction, and employs BEV Track Recovery to continue existing tracks with low‑confidence detections. On the WildTrack dataset, GRACE raises MOTA from 83.54 to 91.07 compared to the baseline TrackTacular.
By Taigo Sakai, Kazuhiro Hotta, Hiroki Kouno, Naoki Kato
arXiv:2606. 10940v1 Announce Type: cross Abstract: Camera traps have become a cornerstone of biodiversity monitoring, but the artificial intelligence that turns vast quantities of images into usable ecological data is often locked behind commercial platforms or trained on fauna that does not match that of the British Isles.
By Paul Fergus, Philip Stephens, Russell A. Hill, Lee Oliver, Katie Appleby, Sarah Beatham, Naomi Davies Walsh, Stuart Nixon, Naomi Matthews, Chris Sutherland, Kelly Hitchcock
arXiv:2509. 08421v2 Announce Type: replace-cross Abstract: For multimedia spatial intelligence through time, multi-view multi-object tracking (MVMOT) suffers from persistent challenges in maintaining consistent object identities across different camera views, leading to tracking inaccuracies.
By Keisuke Toida, Taigo Sakai, Takeshi Nakamura, Hiroshi Shimizu, Kazuhiro Hotta
arXiv:2608.28663v1 Announce Type: new
Abstract: Reliable individual cattle identification supports disease surveillance, vaccination records, breeding management, and livestock insurance. Although th...
By Lalit BC, Dharmendra Singh Chaudhary, Shovit Nepal
arXiv:2606. 23604v2 Announce Type: replace-cross Abstract: The tracking-by-detection paradigm in multi-object tracking (MOT) typically relies on static appearance descriptors to complement motion estimation.
By Mohamed Nagy, Naoufel Werghi, Jorge Dias, Majid Khonji