arXiv AI By Xiaowei Mao, Bowen Sui, Weijie Zhang, Yawen Yang, Shengnan Guo, Shilong Zhao, Jiaqi Lin, Tingrui Wu, Youfang Lin, Huaiyu Wan

Zoom In, Reason Out: Efficient Far-field Anomaly Detection in Expressway Surveillance Videos via Focused VLM Reasoning Guided by Bayesian Inference

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arXiv:2604. 23724v3 Announce Type: replace-cross Abstract: Expressway video anomaly detection is essential for safety management.

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arXiv AI
Jul 10

AUTOPILOT VQA: Benchmarking Vision-Language Models for Incident-Centric Dashcam Understanding

arXiv:2607. 08745v1 Announce Type: new Abstract: Recent advances in Vision-Language Models, Large Language Models, and Multimodal Large Language Models have improved autonomous driving tasks such as scene understanding, decision making, trajectory prediction, and visual question answering.

By Siddharth Damodharan, Radhika Gupta, Ali Alshami, Ryan Rabinowitz, Jugal Kalita
arXiv Computer Vision
6d ago

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

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.

By Xiao Zhang, Wang Zeng, Sheng Jin, Wentao Liu, Chen Qian, Shichao Kan