Interactive Tracking, Prediction, and Behavior Learning of Pedestrians in Dense Crowds

The ability to automatically recognize human motions and behaviors is a key skill for autonomous machines to exhibit to interact intelligently with a human-inhabited environment. The capabilities autonomous machines should have include computing the motion trajectory of each pedestrian in a crowd, predicting his or her position in the near future, and analyzing the personality characteristics of the pedestrian. Such techniques are frequently used for collision-free robot navigation, data-driven crowd simulation, and crowd surveillance applications. However, prior methods for these problems have been restricted to low-density or sparse crowds where the pedestrian movement is modeled using simple motion models. In this thesis, we present several interactive algorithms to extract pedestrian trajectories from videos in dense crowds. Our approach combines different pedestrian motion models with particle tracking and mixture models and can obtain an average of $20\%$ improvement in accuracy in medium-density crowds over prior work. We compute the pedestrian dynamics from these trajectories using Bayesian learning techniques and combine them with global methods for long-term pedestrian prediction in densely crowded settings. Finally, we combine these techniques with Personality Trait Theory to automatically classify the dynamic behavior or the personality of a pedestrian based on his or her movements in a crowded scene. The resulting algorithms are robust and can handle sparse and noisy motion trajectories. We demonstrate the benefits of our long-term prediction and behavior classification methods in dense crowds and highlight the benefits over prior techniques. We highlight the performance of our novel algorithms on three different applications. The first application is interactive data-driven crowd simulation, which includes crowd replication as well as the combination of pedestrian behaviors from different videos. Secondly, we combine the prediction scheme with proxemic characteristics from psychology and use them to perform socially-aware navigation. Finally, we present novel techniques for anomaly detection in low-to medium-density crowd videos using trajectory-level behavior learning.Doctor of Philosoph

Reciprocally-rotating Velocity Obstacles

Modern multi-agent systems frequently use high-level planners to extract basic paths for agents, and then rely on local collision avoidance to ensure that the agents reach their destinations without colliding with one another or dynamic obstacles. One state-of-the-art local collision avoidance technique is Optimal Reciprocal Colli- sion Avoidance (ORCA). Despite being fast and efficient for circular-shaped agents, ORCA may deadlock when polygonal shapes are used. To address this shortcom- ing, we introduce Reciprocally-Rotating Velocity Obstacles (RRVO). RRVO extends ORCA by introducing a notion of rotation. This extension permits more realistic motion than ORCA for polygonally-shaped agents and does not suffer from as much deadlock. In this thesis, we present the theory of RRVO and show empirically that it does not suffer from the deadlock issue ORCA has, that it permits agents to reach goals faster, and that it has a comparable collision rate at the cost of some performance overhead

Efficient, scalable traffic and compressible fluid simulations using hyperbolic models

This thesis presents novel techniques for efficiently animating compressible fluids and traffic flow to improve virtual worlds. I introduce simulation methods that recreate the motion of coupled gas and elastic bodies, shockwaves in compressible gases, and traffic flows on road networks. These can all be described with mathematical models classified as hyperbolic -- models with bounded speeds of information propagation. This leads to parallel computational schemes with very local access patterns. I demonstrate how these models can lead to techniques for physically plausible animations that are efficient and scalable on multi-processor architectures. Animations of gas dynamics, from curling smoke to sonic booms, are visually exciting. Existing computational models of fluids in computer graphics are unsuitable for properly describing compressible gas flows -- I present a method based on a truly compressible model of gas to simulate two-way coupling between gases and elastic bodies on simplicial meshes that can handle large-scale simulation domains in a fast and scalable manner. Computational models of fluids used so far in graphics are inappropriate for describing supersonic gas dynamics because they assume the presence of smooth solutions. I present a technique for the simulation of explosive gas phenomena that addresses the challenges found in animation -- namely stability, efficiency, and generality. I also demonstrate how this method is able to achieve near-linear scaling on modern many-core architectures. Automobile traffic is ubiquitous in modern life; I present a traffic animation technique that uses a hyperbolic continuum model for traffic dynamics and a discrete representation that allows visual depiction and fine control. I demonstrate how this approach outperforms agent-based models for traffic simulation. Additionally, I couple discrete agent-based vehicle simulation to continuum traffic. My hybrid technique captures the interaction between arbitrarily arranged regions of a road network and dynamically transitions between the two models. I present an analysis of the impact my hybrid technique on the ability of simulation to mimic real-world vehicle trajectory data. The methods presented in this dissertation use hyperbolic models for natural and man-made phenomena to open new possibilities for the efficient creation of physically-based animations