VastMAT is a large‑scale multi‑animal tracking benchmark featuring 2,947 videos, 337 animal categories, and over 3.6 million bounding boxes with 22,883 identity trajectories. It emphasizes high‑quality, expert‑reviewed annotations and introduces Seen‑category and Unseen‑category evaluation protocols, revealing significant challenges in tracking unseen animals. The authors also propose a lightweight Center‑Distance‑Augmented Association module that boosts HOTA scores for existing MOT methods without extra training.
arXiv:2606. 07233v1 Announce Type: cross Abstract: LiDAR-based 3D Multi-Object Tracking (MOT) typically relies solely on geometric information, which is often insufficient to distinguish between targets during prolonged occlusions or in crowded human-populated environments.
By Eduardo Borges, Lu\'is Garrote, Urbano J. Nunes
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: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:2606. 07708v1 Announce Type: cross Abstract: We introduce a dataset and benchmark for cross-view urban traffic perception built from synchronized ego-centric bicycle videos and aerial drone videos recorded at real urban intersections.
By Prakhar Bhardwaj, Simone Weikl, Kilian Mang, Elia Jonas Sandtner
arXiv:2606. 14094v1 Announce Type: cross Abstract: Conventional RGB cameras have been widely used in multi-object tracking due to their ability to capture rich appearance and semantic information.
By Shiao Wang, Xiao Wang, Chao Wang, Yitao Li, Menghao Liu, Bo Jiang, Yaowei Wang, Yonghong Tian, Jin Tang
arXiv:2603. 24016v2 Announce Type: replace-cross Abstract: Multi-Object Tracking (MOT) has traditionally focused on a few specific categories, restricting its applicability to real-world scenarios involving diverse objects.
By Zekun Qian, Wei Feng, Ruize Han, Junhui Hou
arXiv:2609.18363v1 Announce Type: new
Abstract: Online multi camera 3D tracking must maintain scene global identities across synchronized views, yet query-based trackers carry these identities only i...
By Pragyan Shrestha, Haruto Nakayama, Atom Scott
The paper investigates how detector architecture, model scale, scene density, and inference regime influence multi‑pedestrian tracking performance. It compares YOLO11, RT‑DETR, and YOLO26 detectors with ByteTrack and BoT‑SORT trackers on MOT17, MOT20, and SOMPT22 datasets, finding RT‑DETR best on HOTA but at higher cost, while YOLO26‑L with NMS balances quality and speed. End‑to‑end inference for YOLO26 did not consistently improve tracking over NMS.
By Pedro Manoel Herminio Alves, Dalton C\'ezane Gomes Valadares, Cl\'audio de Souza Baptista
This paper conducts a systematic empirical study of multi‑object tracking (MOT) algorithms, focusing on how detection and association components affect overall performance. By evaluating state‑of‑the‑art methods on benchmarks such as MOT16/17/20, SportsMOT, DanceTrack, and CrowdTrack, the authors find that detection quality has a far greater impact than association strategies, and that transformer‑based end‑to‑end models are more robust to detection variations but computationally expensive. The study provides a unified pipeline diagram and practical guidance for researchers and practitioners in selecting and designing MOT systems.
By Linh Van Ma, Juhua Hu, Wei Cheng, Unse Fatima, Moongu Jeon
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
WildHSR introduces a lightweight adaptation of 3D foundation models to jointly recover metric cameras, scene geometry, and persistent person identities from monocular video. By generating pseudo‑scale labels from curated web footage and fine‑tuning a Scale Readout, the method predicts metric scale directly from foundation‑model tokens. It also exploits intermediate query‑key features to associate per‑frame bodies, enabling feed‑forward reconstruction that outperforms state‑of‑the‑art optimization‑based methods on several benchmarks while running at 10.1 fps.
By Jerrin Bright, John Zelek