← Back to all news
arXiv Computer Vision By Xiao Zhang, Wang Zeng, Sheng Jin, Wentao Liu, Chen Qian, Shichao Kan

Thinking with Cameras: Active Visual Reasoning via Dynamic Viewpoint Control for Surveillance Video Understanding

Read the original on arXiv Computer Vision →

The paper introduces CamVLM, a framework that equips large vision‑language models with the ability to actively control camera viewpoints for improved surveillance video understanding. It presents two new datasets: CCTV‑Anomaly, a large‑scale surveillance video collection with detailed captions and event annotations, and CamTrack‑53K, an object‑centric viewpoint trajectory dataset for learning camera actions. Using reinforcement learning, CamVLM learns long‑horizon observation strategies, achieving state‑of‑the‑art performance in both passive and dynamic viewpoint settings.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

Related stories

arXiv AI
3d ago

MVVBench: Benchmarking 4D Reasoning in Vision-Language Models

MVVBench is a new benchmark for multi‑view video reasoning that tests vision‑language models on tasks requiring integration of spatial and temporal evidence across multiple, often non‑overlapping camera streams. The benchmark contains questions that cannot be answered from any single view or single moment, forcing models to jointly reason across views and time. It evaluates six capabilities—including attribute identification, relative distance, camera pose, and compositional counting—and provides human‑authored QA, rigorous verification, and detailed error analysis. "whyItMatters":"The benchmark offers a rigorous evaluation of 4D multi‑view reasoning and a foundation for future progress toward reliable embodied perception."

By Hyungjin Chung, Byeongjun Park, Joonseok Lee, Hojun Kim, Jaeho Choi, Byung-Hoon Kim
llmsreinforcement-learningroboticsmultimodalbenchmarks
More like this →
arXiv Machine Learning
Sep 17

ActiveScale: Scaling Active Perception for Robots across Model, Data, and Hardware

ActiveScale is a framework that enhances active perception for robots by integrating model, data, and hardware innovations. It augments vision‑language‑action models with historical video observations and explicit camera‑pose supervision, and introduces a scalable human‑robot mid‑training recipe using 1000 hours of egocentric and robotic data. The Active‑perception Mobile‑manipulation Platform (AMP) enables single‑operator teleoperation for scalable demonstration collection, leading to improved success rates on active‑perception tasks.

By Shuai Zhou, Kaisheng Pang, Wenxuan Song, Wenjie Zhang, Xinhu Zheng, Haoang Li
roboticsmultimodalbenchmarks
More like this →