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Computer Science > Distributed, Parallel, and Cluster Computing

arXiv:2304.02101 (cs)
[Submitted on 4 Apr 2023]

Title:MadEye: Boosting Live Video Analytics Accuracy with Adaptive Camera Configurations

Authors:Mike Wong, Murali Ramanujam, Guha Balakrishnan, Ravi Netravali
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Abstract:Camera orientations (i.e., rotation and zoom) govern the content that a camera captures in a given scene, which in turn heavily influences the accuracy of live video analytics pipelines. However, existing analytics approaches leave this crucial adaptation knob untouched, instead opting to only alter the way that captured images from fixed orientations are encoded, streamed, and analyzed. We present MadEye, a camera-server system that automatically and continually adapts orientations to maximize accuracy for the workload and resource constraints at hand. To realize this using commodity pan-tilt-zoom (PTZ) cameras, MadEye embeds (1) a search algorithm that rapidly explores the massive space of orientations to identify a fruitful subset at each time, and (2) a novel knowledge distillation strategy to efficiently (with only camera resources) select the ones that maximize workload accuracy. Experiments on diverse workloads show that MadEye boosts accuracy by 2.9-25.7% for the same resource usage, or achieves the same accuracy with 2-3.7x lower resource costs.
Comments: 19 pages, 16 figures
Subjects: Distributed, Parallel, and Cluster Computing (cs.DC); Computer Vision and Pattern Recognition (cs.CV); Networking and Internet Architecture (cs.NI)
Cite as: arXiv:2304.02101 [cs.DC]
  (or arXiv:2304.02101v1 [cs.DC] for this version)
  https://doi.org/10.48550/arXiv.2304.02101

Submission history

From: Mike Wong [view email]
[v1] Tue, 4 Apr 2023 19:58:20 UTC (7,604 KB)
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