CAREER: Visual Analysis of High-Dimensional Motion: A Distributed/Collaborative Approach

Northwestern University, Evanston IL

Investigators

Abstract

This project is about analyzing high-dimensional motion (HDM) from video. HDM refers to various complex motions with high degrees of freedom, including the articulation of human body, the deformation of elastic shapes and the multi-motion of multiple occluding targets. The goal of this project is to overcome the curse of dimensionality embedded in this challenging visual inference problem, by systematically pursuing a new distributed/collaborative approach that unifies various HDMs. Substantially different from centralized methods, the new approach distributes HDM into a networked representation of subpart motions, based on Markov network models. Then the prohibitive HDM inference tasks can be effectively and efficiently fulfilled by the "collaborations" among the distributed but mutually constrained small-scale visual inference processes, as revealed by the proposed theoretical study of this model and implemented by the proposed collaborative particle network algorithms. This new approach is expected to be significantly more efficient, more scalable and flexible, and more robust. This project has impact on intelligent video surveillance by making possible fast and accurate human tracking and detection techniques, and significantly benefits the research of human-computer interaction and medical imaging. The research is linked to educational activities aiming at the promotion of learning and innovation through (1) developing an integrated curriculum for visual computing and statistical modeling; (2) motivating students to explore the unknown frontiers via innovative course projects and real-world applications; (3) outreaching to other related research communities via conferences and websites; (4) disseminating the research to the general public, female and minority students by creating Vision OpenHouse events.