Tanmay Randhavane

PedVR: simulating gaze-based interactions between a real user and virtual crowds

We present a novel interactive approach, PedVR, to generate plausible behaviors for a large number of virtual humans, and to enable natural interaction between the real user and virtual agents. Our formulation is based on a coupled approach that combines a 2D multi-agent navigation algorithm with 3D human motion synthesis. The coupling can result in plausible movement of virtual agents and can generate gazing behaviors, which can considerably increase the believability. We have integrated our formulation with the DK-2 HMD and demonstrate the benefits of our crowd simulation algorithm over prior decoupled approaches. Our user evaluation suggests that the combination of coupled methods and gazing behavior can considerably increase the behavioral plausibility.

GLMP- realtime pedestrian path prediction using global and local movement patterns

We present a novel real-time algorithm to predict the path of pedestrians in cluttered environments. Our approach makes no assumption about pedestrian motion or crowd density, and is useful for short-term as well as long-term prediction. We interactively learn the characteristics of pedestrian motion and movement patterns from 2D trajectories using Bayesian inference. These include local movement patterns corresponding to the current and preferred velocities and global characteristics such as entry points and movement features. Our approach involves no precomputation and we demonstrate the real-time performance of our prediction algorithm on sparse and noisy trajectory data extracted from dense indoor and outdoor crowd videos. The combination of local and global movement patterns can improve the accuracy of long-term prediction by 12-18% over prior methods in high-density videos.

Aggressive, Tense or Shy? Identifying Personality Traits from Crowd Videos

We present a real-time algorithm to automatically classify the dynamic behavior or personality of a pedestrian based on his or her movements in a crowd video. Our classification criterion is based on Personality Trait Theory. We present a statistical scheme that dynamically learns the behavior of every pedestrian in a scene and computes that pedestrian’s motion model. This model is combined with global crowd characteristics to compute the movement patterns and motion dynamics, which can also be used to predict the crowd movement and behavior. We highlight its performance in identifying the personalities of different pedestrians in lowand high-density crowd videos. We also evaluate the accuracy by comparing the results with a user study.

SocioSense: Robot navigation amongst pedestrians with social and psychological constraints

We present a real-time algorithm, SocioSense, for socially-aware navigation of a robot amongst pedestrians. Our approach computes time-varying behaviors of each pedestrian using Bayesian learning and Personality Trait theory. These psychological characteristics are used for long-term path prediction and generating proxemic characteristics for each pedestrian. We combine these psychological constraints with social constraints to perform human-aware robot navigation in low- to medium-density crowds. The estimation of time-varying behaviors and pedestrian personalities can improve the performance of long-term path prediction by 21%, as compared to prior interactive path prediction algorithms. We also demonstrate the benefits of our socially-aware navigation in simulated environments with tens of pedestrians.

Predicting power needs in smart grids

Smart grids are becoming ubiquitous today with proliferation of easy to install power generation schemes for Solar and Wind energy. The goal of consuming energy generated locally instead of transmitting it over large distances calls for systems that can process millions of events being generated from smart plugs and power generation sources in near real time. The heart of these systems often is a module that can process dense power consumption event streams and predict the consumption patterns at specific occupational units such as a house or a building. It is also often useful to identify outliers that are consuming power significantly higher than other similar devices in the occupational unit (such as a block or a neighbourhood). In this paper, we present a system that can process over a million events per second from smart plugs and correlate the information to output both accurate predictions as well as identify outliers. Our system is built from the ground up in C++ achieving very high throughput with very low CPU capacity for processing events. Our results show that the throughput of our system is over a million events per second while using under 20% of one core.