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 play 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 can be useful in a variety of applications. In this work, we show its utility in three applications, namely person re-identification, camera placement optimization, and human tracking. 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 accuracy of trajectory forecasting and the computer vision applications.Computer Science, Department o

Agoraphilic navigation algorithm in dynamic environment

This thesis presents a novel Agoraphilic (free space attraction [FSA])-based navigation algorithm. This new algorithm is capable of undertaking local path planning for robot navigation in static and dynamic environments with the presence of a moving goal. The proposed algorithm eliminates the common weaknesses of the existing navigation approaches when operating in unknown dynamic environments while using the modified Agoraphilic concept. The Agoraphilic Navigation Algorithm in Dynamic Environment (ANADE) presented in this thesis does not look for obstacles (problems) to avoid; rather, it looks for free space (solutions) to follow. Therefore, this algorithm is also a human-like optimistic navigation algorithm. The proposed algorithm creates a set of Free Space Forces (FSFs) based on the current and future growing free space around the robot. These Free Space Forces are focused towards the current and future locations of a moving goal and finally generate a single attractive force. This attractive force pulls the robot through current free space towards the future growing free space leading to the goal. The new free space concept allows the ANADE to overcome many common problems of navigation algorithms. Several versions of the ANADE have been developed throughout this research to overcome the main limitation of the original Agoraphilic algorithm and address the common weaknesses of the existing navigation approaches. The ANADE I uses an object tracking method to identify the states (locations) of moving objects accurately. The ANADE II uses a dynamic obstacle prediction methodology to identify the robot’s future environments. In the ANADE III, a novel controller based on fuzzy logic was developed and combined with the new FSA concept to provide optimal navigational solutions at a low computational cost. In the ANADE III, the effectiveness of the ANADE II was further improved by incorporating the velocity vectors of the moving objects into decision-making. In the ANADE IV, a self-tuning system was successfully applied to the ANADE III to take advantage of the performances of free space attraction-based navigation algorithms. The proposed final version of the algorithm (ANADE V) comprises nine main modules. These modules are repeatedly used to create the robot’s driving force, which pulls the robot towards the goal (moving or static). An obstacle tracking module is used to identify the time-varying free spaces by tracking the moving objects. Further, a tracking system is also used to track the moving goal. The capacity of the ANADE was strengthened further by obstacle and goal path prediction modules. Future location prediction allowed the algorithm to make decisions by considering future environments around the robot. This is further supported by a self-tuning, machine learning–based controller designed to efficiently account for the inherent high uncertainties in the robot’s operational environment at a reduced computational cost. Experimental and simulation-based tests were conducted under dynamic environments to validate the algorithm. Further, the ANADE was benchmarked against other recently developed navigation algorithms. Those tests were focused on the behaviour of the algorithm under challenging environments with moving and static obstacles and goals. Further, the test results demonstrate that the ANADE is successful in navigating robots under unknown, dynamically cluttered environments.Doctor of Philosoph