arXiv:2606. 17897v1 Announce Type: new Abstract: Long-term human path forecasting in crowds is critical for autonomous moving platforms (like autonomous driving cars and social robots) to avoid collision and make high-quality planning.
By Xiaodan Shi
arXiv:2507. 00028v2 Announce Type: replace Abstract: The representation of urban trajectory data plays a critical role in effectively analyzing spatial movement patterns.
By Lihuan Li, Hao Xue, Shuang Ao, Yang Song, Flora Salim
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
The paper introduces Ctrl-CWM, a multi‑agent controllable crowd world model that merges crowd generation with run‑time control. It uses an encoder to learn human motion dynamics, an actor to propose pedestrian displacements, a critic to evaluate imagined trajectories, and a planner that selects actions based on critic scores and user‑defined costs. Experiments show that Ctrl‑CWM outperforms state‑of‑the‑art methods in realism and collision metrics while allowing new control objectives to be added without retraining.
By JunGyu Lee, Jisu Shin, Seunghyun Shin, Hae-Gon Jeon
The paper introduces Object-Conditioned Social Diffusion (OCSD), a conditional diffusion model that unifies motion history, multi‑person interactions, and object cues for human motion forecasting in complex scenes. OCSD employs an object‑conditioning mechanism that modulates denoising at each timestep, enabling fine‑grained human‑object reasoning, and a social encoder that captures interactions among all humans. Experiments on the Humans in Kitchens (HiK) and HOI‑M3 benchmarks show state‑of‑the‑art performance, reducing two‑second path error by 31.3% on HiK and 33.2% on HOI‑M3 compared to prior work, while producing more realistic long‑term forecasts.
By Serdar Ozsoy, Lars Doorenbos, Juergen Gall
TrajFusionNet+ is a transformer-based model that predicts pedestrian crossing intention by fusing sequential trajectory data, visual trajectory overlays, and graph-based scene context. It extends the earlier TrajFusionNet with three attention modules—Sequence, Visual, and Graph—to capture temporal, visual, and relational cues. The model outperforms state‑of‑the‑art methods on the PIE and JAAD datasets and shows better generalization under a joint‑training, separate‑evaluation protocol.
By Fran\c{c}ois G. Landry, Moulay A. Akhloufi