UC San Diego

Automated and Distributed-Camera Crowd Analysis is an impressive and important research challenge that has recently gained prominence in our society. Its applications include increased security and efficiency of public environments, research in herd and flocking behavior, population monitoring, urban architecture, and also marketing. However, there exists a striking difference between the environments where we deploy and where we develop these analytics, resulting in non-robust analytics. For the purpose of elucidating prevalent challenges faced by SVCL video crowd analytics, we develop a scalable, distributed and automated research platform composed of three sub-solutions : (1) Acquire data from a dynamic and distributed-camera environment; (2) Automatically compute crowd-count estimates based on privacy-preserving holistic motion segmentation; and (3) Visualize results geo-spatially and temporally on interactive maps. Rather than placing comparative-emphasis on computation methods, however, we consider the influence and limitations our research community's video databases pose. Most pronounced is their static and finite nature, which may be a myopic characteristic constricting our research efforts. Therefore, we contrast results and note the added dynamic and long-term utility provided by our automated platform. Our current video database yields geo- spatial real-time statistics of pedestrian traffic as well as long-term temporal trends over a distributed and connected geographic area. By designing a rich, scalable, and common experimental environment, we can more rigorously evaluate machine vision techniques and crowd dynamics. Attention may be shifted away from evaluation based solely on accuracy and more readily towards the inclusion of technique break-down