Ganesh Ananthanarayanan
ABSTRACT
The world is witnessing an unprecedented increase in camera deployment. The USA and UK, for instance, have one camera for every 8 people. Video analytics from these cameras are becoming more and more pervasive, exerting important functions on a wide range of verticals including manufacturing, transportation, and retails. While vision techniques have seen considerable advancement, they have come at the expense of compute and network cost.
- Chien-Chun Hung, Ganesh Ananthanarayanan, Peter Bodík, Leana Golubchik, Minlan Yu, Victor Bahl, Matthai Philipose. 2018. VideoEdge: Processing Camera Streams using Hierarchical Clusters. In ACM SEC.Google Scholar
- Haoyu Zhang, Ganesh Ananthanarayanan, Peter Bodík, Matthai Philipose, Victor Bahl, Michael Freedman. 2017. Live Video Analytics at Scale with Approximation and Delay-Tolerance. In USENIX NSDI. Google ScholarDigital Library
- Junchen Jiang, Ganesh Ananthanarayanan, Peter Bodík, Siddhartha Sen, Ion Stoica. 2018. Chameleon: Scalable Adaptation of Video Analytics. In ACM SIGCOMM. Google ScholarDigital Library
- Kevin Hsieh, Ganesh Ananthanarayanan, Peter Bodík, Shivaram Venkataraman, Victor Bahl, Matthai Philipose, Phillip B. Gibbons, Onur Mutlu. 2018. Focus: Querying Large Video Datasets with Low Latency and Low Cost. In USENIX OSDI. Google ScholarDigital Library
- Microsoft. 2018. Azure Brainwave . https://docs.microsoft.com/en-us/azure/machine-learning/service/concept-accelerate-with-fpgas .Google Scholar
- Joseph Redmon. 2018. YOLO: Real-Time Object Detection . https://pjreddie.com/darknet/yolo/.Google Scholar
- Samvit Jain, Ganesh Ananthanarayanan, Junchen Jiang, Yuanchao Shu, Joseph E. Gonzalez. 2019. Scaling Video Analytics Systems to Large Camera Deployments. In ACM HotMobile.Google Scholar
Index Terms
- Video Analytics - Killer App for Edge Computing
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