MAETrack introduces a lightweight framework to adapt pretrained masked autoencoder (MAE) representations for 3D single object tracking (SOT). It uses Layer‑Selective Initialization (LSI) to keep shallow geometric layers from the pre‑training while re‑initializing deeper layers, and Geometric Residual Gating (GRG) to emphasize salient regions in BEV features before template‑search fusion. Experiments on standard 3D SOT benchmarks show consistent improvements over vanilla fine‑tuning with minimal computational cost.
By Sifan Zhou, Qiwei Wang, Linyue Tan, Ziyu Liu, Ziyu Zhao, Xiaobo Lu
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: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
Tetris is a video object tracking system that uses tile-level sampling to efficiently extract high‑fidelity tracks. It partitions videos into tile‑based polyominoes, classifies relevant tiles, prunes redundant ones with an ILP under a user‑defined accuracy constraint, and packs the remaining polyominoes to minimize detector calls. On seven stationary‑video datasets, Tetris maintains less than a 5% loss in tracking accuracy while achieving up to 17.4× higher throughput than prior systems and up to 68.8× higher than a full‑frame reference pipeline.
By Chanwut Kittivorawong, Alena Chao, Charlie Si, Alvin Cheung
arXiv:2608.24365v1 Announce Type: new
Abstract: Transformer-based object trackers are renowned for their strong performance, yet dense token processing often leads to prohibitive computational cost,...
By Qingmao Wei, Fagui Liu, Dengke Zhang, Qingze He, Quan Tang
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
Transformer-based object trackers are renowned for their strong performance, yet dense token processing often leads to prohibitive computational cost, limiting real-time deployment on edge devices. Wh...
TQD-Track introduces Temporal Query Denoising (TQD) for 3D Multi‑Object Tracking, extending query denoising beyond single frames by initializing denoising queries from previous‑frame ground truths and propagating them as independent association candidates. The method enriches track queries with temporal context and instance‑specific features, and incorporates diverse noise types to emulate real‑world tracking challenges. Experiments on nuScenes and Argoverse 2 show consistent improvements across multiple MOT baselines with only training‑process modifications.
By Yutong Yang, Shuxiao Ding, Mohammed Amine Bencheikh Lehocine, Julian Wiederer, Markus Braun, Peizheng Li, Juergen Gall, Bin Yang
arXiv:2608.22064v1 Announce Type: new
Abstract: We present our solution for the MOSEv2 track of the 8th Large-scale Video Object Segmentation (LSVOS) Challenge at ECCV 2026. The challenge evaluates r...
By Mingqi Gao, Sijie Li, Jungong Han
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
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:2508.17488v4 Announce Type: replace
Abstract: This paper tackles the critical challenge of optimizing multi-modality trackers by effectively adapting pre-trained models for RGB data. Existing f...
By Zhiwen Chen, Jinjian Wu, Zhiyu Zhu, Yifan Zhang, Guangming Shi, Junhui Hou