An Behavior-based Robotics
The Psychology of Human-Computer Interaction
The Psychology of Human-Computer Interaction
A Dynamical Model of Visually-Guided Steering, Obstacle Avoidance, and Route Selection
International Journal of Computer Vision - Special Issue on Computational Vision at Brown University
An Introduction to the Kalman Filter
An Introduction to the Kalman Filter
Predictive human performance modeling made easy
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Extracting Places and Activities from GPS Traces Using Hierarchical Conditional Random Fields
International Journal of Robotics Research
Real-time hand tracking using a mean shift embedded particle filter
Pattern Recognition
Location-based activity recognition
Location-based activity recognition
Robust Real-Time Unusual Event Detection using Multiple Fixed-Location Monitors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Navigate like a cabbie: probabilistic reasoning from observed context-aware behavior
UbiComp '08 Proceedings of the 10th international conference on Ubiquitous computing
A novel sequence representation for unsupervised analysis of human activities
Artificial Intelligence
PERCOM '09 Proceedings of the 2009 IEEE International Conference on Pervasive Computing and Communications
Learning and inferring transportation routines
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Simultaneous team assignment and behavior recognition from spatio-temporal agent traces
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Networked distributed POMDPs: a synthesis of distributed constraint optimization and POMDPs
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 1
Maximum entropy inverse reinforcement learning
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 3
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One of the most powerful constraints governing many activity recognition problems is that imposed by the human actor. It is well known that humans have a large set of physical and cognitive limitations that constrain their execution of various tasks. In this article, we show how prior knowledge of these perception and locomotion limitations can be exploited to enhance path prediction and tracking in indoor environments for pervasive computing applications. We demonstrate an approach for path prediction based on a model of visually guided steering that has been validated on human obstacle avoidance data. Our approach outperforms standard motion models in a particle filter tracker during occlusion periods of greater than one second and results in a significant reduction in SSD tracking error.