Active Visual Perception: Opportunities and Challenges

2025/12/03 by Yian Li, Xiaoyu Guo, Li, Yian +7
Computer Science · Engineering · Neuroscience · #Advanced Vision and Imaging #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Robotics and Sensor-Based Localization #Tactile and Sensory Interactions

paper · pdf · doi:10.48550/arxiv.2512.03687

openalex publication_date 2025/12/03 · openalex created_date 2025/12/05 · openalex updated_date 2026/07/28

Abstract

Active visual perception refers to the ability of a system to dynamically engage with its environment through sensing and action, allowing it to modify its behavior in response to specific goals or uncertainties. Unlike passive systems that rely solely on visual data, active visual perception systems can direct attention, move sensors, or interact with objects to acquire more informative data. This approach is particularly powerful in complex environments where static sensing methods may not provide sufficient information. Active visual perception plays a critical role in numerous applications, including robotics, autonomous vehicles, human-computer interaction, and surveillance systems. However, despite its significant promise, there are several challenges that need to be addressed, including real-time processing of complex visual data, decision-making in dynamic environments, and integrating multimodal sensory inputs. This paper explores both the opportunities and challenges inherent in active visual perception, providing a comprehensive overview of its potential, current research, and the obstacles that must be overcome for broader adoption.

Citations

• On Inherent Adversarial Robustness of Active Vision Systems
• Understanding Policy and Technical Aspects of AI-Enabled Smart Video Surveillance to Address Public Safety
• On Deep Recurrent Reinforcement Learning for Active Visual Tracking of Space Noncooperative Objects
• Zhuyi: Perception Processing Rate Estimation for Safety in Autonomous Vehicles
• Active Learning for Deep Visual Tracking
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• Deep Bayesian Unsupervised Lifelong Learning
• Context-Aware Task Handling in Resource-Constrained Robots with Virtualization
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• Deep Reinforcement Learning based Resource Allocation for V2V Communications
• Deep Reinforcement Learning Based Resource Allocation for V2V Communications
• Multi-View 3D Object Detection Network for Autonomous Driving

Related

• Revisiting Active Perception
• Active Perception with Neural Networks
• Vision in Action: Learning Active Perception from Human Demonstrations
• Steps Towards a Theory of Visual Information: Active Perception, Signal-to-Symbol Conversion and the Interplay Between Sensing and Control
• Active-O3: Empowering Multimodal Large Language Models with Active Perception via GRPO