Although persistent and long-duration tracking of general targets is a basic function in the human vision system, this task is quite challenging for computer vision algorithms, because the visual appearances of real world targets vary greatly and the environments are heavily cluttered and distractive. This large gap has been a bottleneck in many video analysis applications. This project aims to bridge this gap and to overcome the challenges that confront the design of long-duration tracking systems, by developing new computational models to integrate and represent some important aspects in the human visual perception of dynamics, including selective attention and context-awareness that have been largely ignored in existing computer vision algorithms.
This project performs in-depth investigations of a new computational paradigm, called the synergetic selective attention model that integrates four processes: the early selection process that extracts informative attentional regions (ARs), the synergetic tracking process that estimates the target motion based on these ARs, the robust integration process that resolves the inconsistency among the motion estimates of these ARs for robust information fusion, and the context-aware learning process that performs late selection and learning on-the-fly to discover contextual associations and to learn discriminative-ARs for adaptation.
This research enriches the study of visual motion analysis by accommodating aspects from the human visual perception and leads to significant improvements for video analysis. It benefits many important areas including intelligent video surveillance, human-computer interaction and video information management. The project is linked to educational activities to promote learning and innovation through curriculum development, research opportunities, knowledge dissemination through conferences and the internet as well as other outreach activities, and the involvements of underrepresented groups.
