Accurately Measuring Viewer Attention for Maximum Marketing Impact Using Computer Vision


Where: Theater

Date:  Day 2

Start Time: 2:20 pm

End Time: 2:50 pm

Traditional techniques to measure television viewership have relied on active engagement from the audience in the form of button clicks and surveys. Not only do these techniques bias the captured data, they also fail to measure viewers’ attention levels as they cannot deduce whether viewers are actually in front of the TV, and if so, whether they are actually watching. This talk describes how TVision is leveraging the latest advances in deep learning and computer vision to measure viewer attention on a per-second basis and categorize attentive reach by demographic, time of day, channel and program.

Track

Session Speakers

  • Vinod Kathail is a Xilinx Fellow and the Chief Architect for the SDx Development Environment. He is also leading the company-wide focus on embedded vision including machine learning usage in edge and endpoint applications. He initiated the software programmability effort for the Zynq family, and developed and drove the adoption of SDSoC earlier on. Prior to joining Xilinx, Vinod was the founding CEO and later CTO of Synfora, a high-level synthesis startup. Vinod brings over 25 years of experience in heterogeneous programming environments, high-performance parallel and VLIW architectures, parallelizing compilers and high-level synthesis, working in both research labs (HP Labs) and startups. Vinod received a B. Tech in Electrical Engineering from MANIT, Bhopal, a M. Tech in Computer Science from IIT, Kanpur and an ScD in Electrical Engineering and Computer Science from MIT. He holds over 25 patents, and he has authored numerous research publications.


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Accurately Measuring Viewer Attention for Maximum Marketing Impact Using Computer Vision