Final submissions due: Oct 1, 2018
Publication issue: July/August 2019
Guest Editors: Ganesh Ananthanarayanan (Microsoft Research) and Weisong Shi (Wayne State University). Contact them at email@example.com
Cameras are everywhere! According to a January 2013 report published by the National Public Radio, the Chinese government has installed more than 20 million cameras across the country. Similarly, a recent BBC report stated, there is one camera for every 14 people in London. Other large cities including New York, Paris, and Tokyo are also deploying cameras in large numbers, so it is reasonable to claim that networked cameras are everywhere. These cameras are deployed for a wide variety of commercial and security reasons. Further, consumer devices themselves have cameras with users interested in streaming videos live from these devices. We are all living in the golden era for computer vision and AI, with many recent advancements combined with copious training data and systems infrastructure, largely improving their range of capabilities. Analyzing live videos from these cameras has great potential to impact science and society. Live video analytics has the potential to impact a wide range of verticals ranging from public safety, traffic efficiency, infrastructure planning, entertainment, and home safety.
Analyzing live video streams is arguably the most challenging of domains for systems-for-AI. Unlike text or numeric processing, video analytics require higher bandwidth, consume considerable CPU and GPU resources for processing, necessitate richer query semantics, and demand tighter security & privacy guarantees. The widespread need for video analytics and abundance of video data presents the unique and timely opportunity to design solutions for live video analytics. All aspects of video analytics call to be designed “green-field”, from vision algorithms, to the systems processing stack and networking infrastructure, including hybrid cloud solutions. Such a holistic design will enable the democratization of live video analytics such that any organization with cameras can obtain value from video analytics.
This special issue calls for research on various issues and solutions that can enable live video analytics. Topics of interest include (but aren’t limited) to the following:
- Low cost video analytics
- Deployment experience with large array of cameras
- Storage of video data and metadata
- Network design for video streams
- Hybrid cloud (or edge-based) solution for video processing
- Distributed systems for large-scale video processing
- Scheduling for multi-tenant video processing
- Training and inference of vision neural networks
- Processor architectures for video processing
- Energy-efficient system design for video analytics
- Intelligent camera designs
- Video analytics for social good
- Secure processing of video analytics
- Privacy-preserving techniques for video processing
- Interactive querying of video streams
All submissions must be original manuscripts of fewer than 5,000 words, focused on Internet technologies and implementations. All manuscripts are subject to peer review on both technical merit and relevance to IC’s international readership—primarily practicing engineers and academics who are looking for material that introduces new technology and broadens familiarity with current topics. We do not accept white papers, and papers which are primarily theoretical or mathematical must clearly relate the mathematical content to a real-life or engineering application. To submit a manuscript, please log on to ScholarOne (https://mc.manuscriptcentral.com/ic-cs) to create or access an account, which you can use to log on to IC’s Author Center and upload your submission.