Learning to Select Camera Views: Efficient Multiview Understanding at Few Glances
Keywords: Multi-view classification; multi-view detection; efficient algorithm; reinforcement learning
Abstract: Multiview camera setups have proven useful in many computer vision applications for reducing ambiguities, mitigating occlusions, and increasing field-of-view coverage. However, the high computational cost associated with multiple views creates a significant challenge for end devices with limited computational resources. To address this issue, we propose a view selection approach that analyzes the target object or scenario from given views and selects the next-best-view for recognition or detection. Our approach features a reinforcement learning based camera selection module, MVSelect, that not only selects views but also facilitates joint training with the task network. Experimental results on multiview classification and detection tasks show that our approach achieves promising performance while using only 2 or 3 out of N available views, significantly reducing computational costs. Furthermore, analysis on the selected views reveals that certain cameras can be shut off with minimal performance impact, shedding light on future camera layout optimization for multiview systems.
Primary Area: representation learning for computer vision, audio, language, and other modalities
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Submission Number: 1564
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