The paper introduces MovingDroneCrowd++, a large-scale video dataset for dense crowd counting and tracking from moving drones, featuring varied flight altitudes, camera angles, and lighting. It presents two new methods: GD3A for Video Individual Counting and GIA-Track for Multi-Object Tracking, both leveraging group-wise density assignment and identity association to handle aerial challenges. Experiments demonstrate significant improvements, reducing counting error by 47.4% and boosting tracking accuracy by 64.6%.
By Yaowu Fan, Jia Wan, Tao Han, Andy J. Ma, Wanli Ouyang, Antoni B. Chan
arXiv:2606. 01694v1 Announce Type: cross Abstract: Thermal pedestrian MOT remains challenging because weak appearance cues and frequent detection interruptions cause severe trajectory fragmentation.
By Wei-Chieh Sun, Gyungmin Ko, Heejae Kwon, Hsiang-Wei Huang, Jenq-Neng Hwang
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
arXiv:2511.16949v2 Announce Type: replace-cross
Abstract: Dense 3D semantic occupancy perception is critical for mobile robots operating in pedestrian-rich environments, yet it remains underexplored...
By Junseo Kim, Guido Dumont, Xinyu Gao, Gang Chen, Holger Caesar, Javier Alonso-Mora
arXiv:2609.39467v1 Announce Type: new
Abstract: Pedestrian detection plays a crucial role in computer vision with applications in autonomous driving, surveillance, and public safety. However, real-wo...
By ZiAn Wang, MingZhe Liu, Chaoyi Guo, ChangChun Li, Fangming Gu
arXiv:2609.14419v1 Announce Type: cross
Abstract: Person re-identification (ReID) is essential for multi-camera surveillance and tracking, yet remains difficult due to viewpoint and illumination chan...
By Leon Fernando, C Dombawala, P. Hettigoda, Vanodhya G. Warnasooriya, Ishara Neranjana, Rashmika Nawaratne
arXiv:2607. 19036v1 Announce Type: cross Abstract: V2X collaborative object detection features overcoming the limitations of single-vehicle systems by aggregating environmental features from multiple collaborative agents.
By Zhihao Yang, Zhiyu Xiang, Peng Xu, Tianyu Pu, Kai Wang, Eryun Liu, Dongping Zhang, Yong Ding
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
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:2506. 22784v2 Announce Type: replace-cross Abstract: Point-pixel registration between LiDAR point clouds and camera images is a fundamental yet challenging task in autonomous driving and robotic perception.
By Yu Han, Zhiwei Huang, Yanting Zhang, Fangjun Ding, Shen Cai, Xiaoyu Tang, Yanchao Dong, Rui Fan
arXiv:2606. 01277v1 Announce Type: cross Abstract: Current end-to-end autonomous driving systems predominantly rely on frame-based sensors, which suffer from inherent perception latency and motion blur during highly dynamic encounters, specifically sudden pedestrian crossings.
By Oskar Natan, Andi Dharmawan, Aufaclav Zatu Kusuma Frisky, Jazi Eko Istiyanto, Jun Miura
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