Modeling and Inferring Human Intents and Latent Functional Objects for Trajectory Prediction
This addresses the challenge of understanding human behavior in public spaces for surveillance applications, but is incremental as it builds on existing trajectory prediction methods with a novel modeling approach.
The paper tackles the problem of predicting human trajectories and inferring intentions in surveillance videos by modeling people as agents moving toward functional objects, and demonstrates effectiveness in predicting intent behaviors, localizing objects, and discovering object classes through a new annotated dataset.
This paper is about detecting functional objects and inferring human intentions in surveillance videos of public spaces. People in the videos are expected to intentionally take shortest paths toward functional objects subject to obstacles, where people can satisfy certain needs (e.g., a vending machine can quench thirst), by following one of three possible intent behaviors: reach a single functional object and stop, or sequentially visit several functional objects, or initially start moving toward one goal but then change the intent to move toward another. Since detecting functional objects in low-resolution surveillance videos is typically unreliable, we call them "dark matter" characterized by the functionality to attract people. We formulate the Agent-based Lagrangian Mechanics wherein human trajectories are probabilistically modeled as motions of agents in many layers of "dark-energy" fields, where each agent can select a particular force field to affect its motions, and thus define the minimum-energy Dijkstra path toward the corresponding source "dark matter". For evaluation, we compiled and annotated a new dataset. The results demonstrate our effectiveness in predicting human intent behaviors and trajectories, and localizing functional objects, as well as discovering distinct functional classes of objects by clustering human motion behavior in the vicinity of functional objects.