Chameleon: scalable adaptation of video analytics

By Junchen Jiang, Ganesh Ananthanarayanan, Peter Bodík, Siddhartha Sen, Ion Stoica. Conference on Applications, Technologies, Architectures, and Protocols for Computer Communication, 2018

520 citations recorded by the source corpus.

Applying deep convolutional neural networks (NN) to video data at scale poses a substantial systems challenge, as improving inference accuracy often requires a prohibitive cost in computational resources. While it is promising to balance resource and accuracy by selecting a suitable NN configuration (e.g., the resolution and frame rate of the input video), one must also address the significant dynamics of the NN configuration's impact on video analytics accuracy. We present Chameleon, a controller that dynamically picks the best configurations for existing NN-based video analytics pipelines. The key challenge in Chameleon is that in theory, adapting configurations frequently can reduce resource consumption with little degradation in accuracy, but searching a large space of configurations periodically incurs an overwhelming resource overhead that negates the gains of adaptation. The insight behind Chameleon is that the underlying characteristics (e.g., the velocity and sizes of objects) that affect the best configuration have enough temporal and spatial correlation to allow the search cost to be amortized over time and across multiple video feeds. For example, using the video feeds of five traffic cameras, we demonstrate that compared to a baseline that picks a single optimal configuration offline, Chameleon can achieve 20-50% higher accuracy with the same amount of resources, or achieve the same accuracy with only 30--50% of the resources (a 2-3X speedup).

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/26ae0f7d0939d594ffb590a288ffa30f168cf603

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Citations in the corpus

Convergence of Edge Computing and Deep Learning: A Comprehensive Survey (2019, methodology): “In order to adjust the configuration of the input video stream (such as frame resolution and sampling rate) online with low cost, Chameleon [138] greatly saves the cost of searching the best model configuration by leveraging temporal and spatial correlations of the video inputs, and allows the cost”
Deep Learning With Edge Computing: A Review (2019, methodology): “[120] M. S. Riazi, C. Weinert, O. Tkachenko, E. M. Songhori, T. Schneider, and F. Koushanfar, “Chameleon: A hybrid secure computation framework for machine learning applications,” in Proc.”
Reducto: On-Camera Filtering for Resource-Efficient Real-Time Video Analytics (2020, methodology): “We also compared Reducto with two systems that improve the efficiency of real-time video analytics pipelines, CloudSeg [57] and Chameleon [41].”
Edge Assisted Real-time Object Detection for Mobile Augmented Reality (2019, background): “VideoStorm [45] and Chameleon [46] achieve higher accuracy video analytics with the same amount of computational resources on the cloud by adapting the video configurations.”
Online Model Distillation for Efficient Video Inference (2018, background): “) Unlike other datasets for efficient inference, which consist of streams from fixed-viewpoint cameras such as traffic cameras [19], we capture a diverse array of challenges: from fixed-viewpoint cameras, to constantly moving and zooming television cameras, and hand-held and egocentric video.”