2019 Proceeding- General Chairs:
- Ganesh Ananthanarayanan,
- Yunxin Liu,
- Yuanchao Shu
It is our great pleasure to welcome you to the 2019 ACM Workshop on Hot Topics in Video Analytics and Intelligent Edges - HotEdgeVideo'19. The basis for this workshop is the pervasive deployment of cameras everywhere! Analyzing live videos from these cameras has great potential to impact science and society. 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 compute cycles for processing, necessitate richer query semantics, and demand tighter security&privacy guarantees.
This workshop will provide the forum for presentation and discussion of research results and experience reports on upcoming issues of video analytics systems, edge computing, storage of videos, security &privacy implications, as well as novel applications. The mission of the workshop is to enable debate on the challenges and implications of video analytics systems, beneficial application scenarios, and identify new directions for future research and development. HotEdgeVideo gives researchers and practitioners a unique opportunity to share their perspectives with others interested in the various aspects of video analytics.
The call for papers attracted submissions from Europe, China, India, and the United States. The accepted papers cover topics ranging from deep neural networks for video analytics, network considerations in streaming the videos, tamper-proof video processing, and evolution of smart cameras with compute onboard. Please check out the program and we hope you can attend the sessions.
Proceeding Downloads
Networked Cameras Are the New Big Data Clusters
The increasing complexity of deep learning and massive deployment of cameras at the edge have drastically increased the resource demand of edge data analytics. Compared to traditional Internet web applications, such resource demand (in computing, ...
Live Video Analytics with FPGA-based Smart Cameras
Analyzing video feeds from large camera networks requires enormous compute and bandwidth. Edge computing has been proposed to ease the burden by bringing resources to the proximity of data. However, the number of cameras keeps growing and the associated ...
Space-Time Vehicle Tracking at the Edge of the Network
While large number of cameras have been deployed on campus, governments and restricted areas to protect the safety of residents and their properties, manually searching for the track of a suspicious human or vehicle is painful and prone to errors. An ...
Distilled Split Deep Neural Networks for Edge-Assisted Real-Time Systems
Offloading the execution of complex Deep Neural Networks (DNNs) models to compute-capable devices at the network edge, that is, edge servers, can significantly reduce capture-to-output delay. However, the communication link between the mobile devices ...
Cracking open the DNN black-box: Video Analytics with DNNs across the Camera-Cloud Boundary
- John Emmons,
- Sadjad Fouladi,
- Ganesh Ananthanarayanan,
- Shivaram Venkataraman,
- Silvio Savarese,
- Keith Winstein
Advancements in deep neural networks (DNNs) and widespread deployment of video cameras have fueled the need for video analytics systems. Despite rapid advances in system design, existing systems treat DNNs largely as "black boxes'' and either deploy ...
secGAN: A Cycle-Consistent GAN for Securely-Recoverable Video Transformation
Video streaming is one of the critical functionalities on edge devices, like IP cameras, because it enables many applications in mobile scenarios, such as live street viewing or surveillance video analytics. In the streaming pipeline, video ...
Client-side Bandwidth Estimation Technique for Adaptive Streaming of a Browser Based Free-Viewpoint Application
High bandwidth demands in interactive streaming applications pose challenges in efficiently utilizing the available bandwidth. Well known standards like MPEG-DASH and Apple HTTP streaming use buffer control mechanisms at the client for bandwidth ...
Sensor Training Data Reduction for Autonomous Vehicles
Ensuring safety and reliability of autonomous vehicles requires good learning models which, in turn, require a large amount of real-world training data. Data produced by in-vehicle sensors (e.g., cameras, LIDARs, IMUs, etc.) can be used for training; ...