Building upon state-of-the-art algorithms for pedestrian detection and multi-object tracking,
and inspired by sociological models of human collective behavior, we automatically detect …

Plan recognition, activity recognition, and intent recognition together combine and unify
techniques from user modeling, machine vision, intelligent user interfaces, human/computer …

The world we live in is dynamic by nature. Frequently, the environment changes in ways we
cannot predict. In machine learning, the phenomenon that occurs when a model has its …

We present the PHATT algorithm for plan recognition. Unlike previous approaches to plan
recognition, PHATT is based on a model of plan execution. We show that this clarifies …

Modeling and predicting human and vehicle motion is an active research domain. Owing to
the difficulty in modeling the various factors that determine motion (eg internal state …

Recent work on computer vision analysis of crowds tends to focus on robustly tracking
individuals through the crowd or on analyzing the overall pattern of flow. Our work seeks a …

Event models obtained automatically from video can be used in applications ranging from
abnormal event detection to content based video retrieval. When multiple agents are …

Recognizing activities in real-world videos is a challenging AI problem. We present a novel
combination of standard activity classification, object recognition, and text mining to learn …

This paper extends existing plan recognition research to handle plans containing loops. We
supply an encoding of plans with loops for recognition, based on techniques used to parse …

We present a novel approach for automatic detection of social groups of pedestrians in
crowds. Instead of computing pairwise similarity between pedestrian trajectories, followed by …