Video Stream Analytics Reading List

General

[Computer’17] Real-Time Video Analytics: The Killer App for Edge Computing

Edge-Cloud Hybrid

[MobiCom’15] The Design and Implementation of a Wireless Video Surveillance System
[MobiSys’16] MCDNN: An Execution Framework for Deep Neural Networks on Resource-Constrained Devices
[ASPLOS’17] Neurosurgeon: Collaborative Intelligence Between the Cloud and Mobile Edge
[VLDB’18] Collaborative Edge and Cloud Neural Networks for Real-Time Video Processing
[MLSys’19] Scaling Video Analytics on Constrained Edge Nodes
[HotCloud’19] Bridging the Edge-Cloud Barrier for Real-time Advanced Vision Analytics
[HotEdgeVideo] Cracking open the DNN black-box: Video Analytics with DNNs across the Camera-Cloud Boundary
[SEC’20] Clownfish: Edge and Cloud Symbiosis for Video Stream Analytics
[SIGCOMM’20] Reducto: On-Camera Filtering for Resource-Efficient Real-Time Video Analytics
[INFOCOM’21] Enabling Edge-Cloud Video Analytics for Robotics Applications
[MobiCom’21] Elf: Accelerate High-resolution Mobile Deep Vision with Content-aware Parallel Offloading

Edge-Based

[SenSys’15] Glimpse: Continuous, Real-Time Object Recognition on Mobile Devices
[SEC’17] LAVEA: Latency-aware Video Analytics on Edge Computing Platform
[SEC’18] Bandwidth-efficient Live Video Analytics for Drones via Edge Computing
[ASPLOS’18] The Architectural Implications of Autonomous Driving: Constraints and Acceleration
[INFOCOM’19] Hetero-Edge: Orchestration of Real-time Vision Applications on Heterogeneous Edge Clouds
[MobiCom’19] Edge Assisted Real-time Object Detection for Mobile Augmented Reality
[MLSys’20] SkyNet: a Hardware-Efficient Method for Object Detection and Tracking on Embedded Systems
[MLSys’20] Memory-Driven Mixed Low Precision Quantization For Enabling Deep Network Inference On Microcontrollers
[HotMobile’20] Improving Resource Efficiency of Deep Activity Recognition via Redundancy Reduction
[SIGCOMM’20] Server-Driven Video Streaming for Deep Learning Inference

Cloud-Based

[NSDI’14] Aggregation and Degradation in JetStream: Streaming Analytics in the Wide Area
[NSDI’17] Live Video Analytics at Scale with Approximation and Delay-Tolerance
[SEC’17] ParkMaster: An In–Vehicle, Edge–Based Video Analytics Service for Detecting Open Parking Spaces in Urban Environments
[SEC’18] VideoEdge: Processing Camera Streams using Hierarchical Clusters
[SIGCOMM’18] Chameleon: Scalable Adaptation of Video Analytics
[SIGCOMM’18] AWStream: Adaptive Wide-Area Streaming Analytics
[OSDI’18] Focus: Querying Large Video Datasets with Low Latency and Low Cost
[OSDI’18] Neural Adaptive Content-Aware Internet Video Delivery
[SOSP’19] Nexus: A GPU Cluster Engine for Accelerating DNN-Based Video Analysis

Privacy

[Security’20] Visor: Privacy-Preserving Video Analytics as a Cloud Service
[MobiCom’21] PECAM: Privacy-Enhanced Video Streaming and Analytics via Securely-Reversible Transformation

Camera Virtualization

[IPSN’17] Panoptes: Servicing Multiple Applications Simultaneously using Steerable Cameras
[SEC’18] Application-aware iot camera virtualization for video analytics edge computing

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