In Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy
will defend his dissertation
Contextual Human Trajectory Forecasting within indoor environments and its Applications
A human trajectory is the likely path a human subject would take to get to a destination. Human trajectory forecasting algorithms try to estimate or predict this path. Such algorithms have wide applications in robotics, computer vision and video surveillance. Understanding the human behavior can provide useful information towards the design of these algorithms. Human trajectory forecasting algorithm is an interesting problem because the outcome is influenced by many factors, of which we believe that the destination, geometry of the environment and the humans in it plays a significant role. In addressing this problem, we propose a model to estimate the occupancy behavior of humans based on the geometry and behavioral norms. We also develop a trajectory forecasting algorithm that understands this occupancy and leverages it for trajectory forecasting in previously unseen geometries. The algorithm is embedding into computer vision algorithms, namely person re-identification, camera placement optimization and tracking to demonstrate the significance of it applicability. Experiments were performed with real world data and compared to state-of-the-art methods to assess the quality of the forecasting algorithm and the enhancement in the quality of the applications. Results obtained suggests a significant enhancement in the accur acy of trajectory forecasting and the computer vision applications by incorporating the occupancy behavior model.
Date: Friday, November 6, 2015
Time: 10:00 AM
Place: PGH 550
Advisor: Prof. Shishir Shah
Faculty, students, and the general public are invited.