Can you tell a face from a HEVC bitstream?

Image and video analytics are being increasingly used on a massive scale. Not only is the amount of data growing, but the complexity of the data processing pipelines is also increasing, thereby exacerbating the problem. It is becoming increasingly important to save computational resources wherever possible. We focus on one of the poster problems of visual analytics -- face detection -- and approach the issue of reducing the computation by asking: Is it possible to detect a face without full image reconstruction from the High Efficiency Video Coding (HEVC) bitstream? We demonstrate that this is indeed possible, with accuracy comparable to conventional face detection, by training a Convolutional Neural Network on the output of the HEVC entropy decoder

The Data Gap in Sports Analytics and How to Close It

As the importance and prevalence of sports analytics grows, so does the inequality in sports data. In this paper we examine two main sources of such disparity - the perceived hierarchy of sports and privatization of data. We argue that such inequality hurts the sports analytics community in the short and long terms, and suggest ways for the deep-learning, AI, and sports analytics communities to help mitigate the issue. Keywords: Sports Analytics; AI; Team Sports; Diversit

Online MoCap Data Coding with Bit Allocation, Rate Control, and Motion-Adaptive Post-Processing

With the advancements in methods for capturing 3D object motion, motion capture (MoCap) data are starting to be used beyond their traditional realm of animation and gaming in areas such as the arts, rehabilitation, automotive industry, remote interactions, and so on. As the amount of MoCap data increases, compression becomes crucial for further expansion and adoption of these technologies. In this paper, we extend our previous work on low-delay MoCap data compression by introducing two improvements. The first improvement is the bit allocation to long-term and short-term reference MoCap frames, which provides a 10-15% reduction in coded bitrate at the same quality. The second improvement is the post-processing in the form of motion-adaptive temporal low-pass filtering, which is able to provide another 9-13%savings in the bitrate. The experimental results also indicate that the proposed online MoCap codec is competitive with several state-of-the-art offline codecs. Overall, the proposed techniques integrate into a highly effective online MoCap codec that is suitable for low-delay applications, whose implementation is provided alongside this paper to aid further research in the field