Year: 2025
Type: conference-paper
Abstract: Real-time video analytics on high-resolution cameras has become a popular technology for various intelligent services like traffic control and crowd monitoring. While extensive work has been done on improving analytics accuracy with timing guarantees, virtually all of them target static viewpoint cameras. In this paper, we present Uirapuru, a novel framework for real-time, edge-based video analytics on high-resolution steerable cameras. The actuation performed by those cameras brings significant dynamism to the scene, presenting a critical challenge to existing popular approaches such as frame tiling. To address this problem, Uirapuru incorporates a comprehensive understanding of camera actuation into the system design paired with fast adaptive tiling at a per-frame level. We evaluate Uirapuru on a high-resolution video dataset, augmented by pan-tilt-zoom (PTZ) movements typical for steerable cameras and on real-world videos collected from an actual PTZ camera. Our experimental results show that Uirapuru provides up to 1.45× improvement in accuracy while respecting specified latency budgets or reaches up to 4.53× inference speedup with on-par accuracy compared to state-of-the-art static camera approaches.
Institutions: Vrije Universiteit Amsterdam, Paderborn University
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4416479524