Towards Performance Clarity of Edge Video Analytics

05/18/2021
by   Zhujun Xiao, et al.
0

Edge video analytics is becoming the solution to many safety and management tasks. Its wide deployment, however, must first address the tension between inference accuracy and resource (compute/network) cost. This has led to the development of video analytics pipelines (VAPs), which reduce resource cost by combining DNN compression/speedup techniques with video processing heuristics. Our measurement study on existing VAPs, however, shows that today's methods for evaluating VAPs are incomplete, often producing premature conclusions or ambiguous results. This is because each VAP's performance varies substantially across videos and time (even under the same scenario) and is sensitive to different subsets of video content characteristics. We argue that accurate VAP evaluation must first characterize the complex interaction between VAPs and video characteristics, which we refer to as VAP performance clarity. We design and implement Yoda, the first VAP benchmark to achieve performance clarity. Using primitive-based profiling and a carefully curated benchmark video set, Yoda builds a performance clarity profile for each VAP to precisely define its accuracy/cost tradeoff and its relationship with video characteristics. We show that Yoda substantially improves VAP evaluations by (1) providing a comprehensive, transparent assessment of VAP performance and its dependencies on video characteristics; (2) explicitly identifying fine-grained VAP behaviors that were previously hidden by large performance variance; and (3) revealing strengths/weaknesses among different VAPs and new design opportunities.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 1

page 2

page 3

page 4

12/19/2020

Ekya: Continuous Learning of Video Analytics Models on Edge Compute Servers

Video analytics applications use edge compute servers for the analytics ...
02/05/2021

A Serverless Cloud-Fog Platform for DNN-Based Video Analytics with Incremental Learning

DNN-based video analytics have empowered many new applications (e.g., au...
08/28/2019

ApproxNet: Content and Contention Aware Video Analytics System for the Edge

Videos take lot of time to transport over the network, hence running ana...
09/23/2019

Collaborative Intelligent Cross-Camera Video Analytics at Edge: Opportunities and Challenges

Nowadays, video cameras are deployed in large scale for spatial monitori...
10/03/2018

VStore: A Data Store for Analytics on Large Videos

We present VStore, a data store for supporting fast, resource-efficient ...
11/10/2021

Towards Live Video Analytics with On-Drone Deeper-yet-Compatible Compression

In this work, we present DCC(Deeper-yet-Compatible Compression), one ena...
01/19/2022

GEMEL: Model Merging for Memory-Efficient, Real-Time Video Analytics at the Edge

Video analytics pipelines have steadily shifted to edge deployments to r...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.