TY - GEN
T1 - The role of data-driven priors in multi-agent crowd trajectory estimation
AU - Qiao, Gang
AU - Yoon, Sejong
AU - Kapadia, Mubbasir
AU - Pavlovic, Vladimir
N1 - Publisher Copyright: Copyright © 2018, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
PY - 2018
Y1 - 2018
N2 - Trajectory interpolation, the process of filling-in the gaps and removing noise from observed agent trajectories, is an essential task for the motion inference in a multi-agent setting. A desired trajectory interpolation method should be robust to noise, changes in environments or agent densities, while also being able to yield realistic group movement behaviors. Such realistic behaviors are, however, challenging to model as they require avoidance of agent-agent or agent-environment collisions and, at the same time, demand computational efficiency. In this paper, we propose a novel framework composed of data-driven priors (local, global or combined) and an efficient optimization strategy for multi-agent trajectory interpolation. The data-driven priors implicitly encode the dependencies of movements of multiple agents and the collision-avoiding desiderata, enabling elimination of costly pairwise collision constraints, resulting in reduced computational complexity and often improved estimation. Various combinations of priors and optimization algorithms are evaluated in comprehensive simulated experiments. Our experimental results reveal important insights, including the significance of the global flow prior and the lesser-than-expected influence of data-driven collision priors.
AB - Trajectory interpolation, the process of filling-in the gaps and removing noise from observed agent trajectories, is an essential task for the motion inference in a multi-agent setting. A desired trajectory interpolation method should be robust to noise, changes in environments or agent densities, while also being able to yield realistic group movement behaviors. Such realistic behaviors are, however, challenging to model as they require avoidance of agent-agent or agent-environment collisions and, at the same time, demand computational efficiency. In this paper, we propose a novel framework composed of data-driven priors (local, global or combined) and an efficient optimization strategy for multi-agent trajectory interpolation. The data-driven priors implicitly encode the dependencies of movements of multiple agents and the collision-avoiding desiderata, enabling elimination of costly pairwise collision constraints, resulting in reduced computational complexity and often improved estimation. Various combinations of priors and optimization algorithms are evaluated in comprehensive simulated experiments. Our experimental results reveal important insights, including the significance of the global flow prior and the lesser-than-expected influence of data-driven collision priors.
UR - http://www.scopus.com/inward/record.url?scp=85060456436&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85060456436&partnerID=8YFLogxK
M3 - Conference contribution
T3 - 32nd AAAI Conference on Artificial Intelligence, AAAI 2018
SP - 4710
EP - 4717
BT - 32nd AAAI Conference on Artificial Intelligence, AAAI 2018
PB - AAAI press
T2 - 32nd AAAI Conference on Artificial Intelligence, AAAI 2018
Y2 - 2 February 2018 through 7 February 2018
ER -