Finding Trajectories of Feature Points in a Monocular Image Sequence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Establishing motion correspondence
CVGIP: Image Understanding
Ignorance, myopia, and naivete´ in computer vision systems
CVGIP: Image Understanding
Human motion analysis: a review
Computer Vision and Image Understanding
IEEE Transactions on Pattern Analysis and Machine Intelligence
Perceptual Organization and Visual Recognition
Perceptual Organization and Visual Recognition
Color-Based Tracking of Heads and Other Mobile Objects at Video Frame Rates
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Using Adaptive Tracking to Classify and Monitor Activities in a Site
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
FG '96 Proceedings of the 2nd International Conference on Automatic Face and Gesture Recognition (FG '96)
W4: Who? When? Where? What? A Real Time System for Detecting and Tracking People
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
Camera handoff: tracking in multiple uncalibrated stationary cameras
HUMO '00 Proceedings of the Workshop on Human Motion (HUMO'00)
The KidsRoom: A Perceptually-Based Interactive and Immersive Story Environment
Presence: Teleoperators and Virtual Environments
A survey of advances in vision-based human motion capture and analysis
Computer Vision and Image Understanding - Special issue on modeling people: Vision-based understanding of a person's shape, appearance, movement, and behaviour
ECSQARU '07 Proceedings of the 9th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Fuzzy contour tracking of human silhouettes
FUZZ-IEEE'09 Proceedings of the 18th international conference on Fuzzy Systems
Camera handoff with adaptive resource management for multi-camera multi-object tracking
Image and Vision Computing
International Journal of Approximate Reasoning
Survey on classifying human actions through visual sensors
Artificial Intelligence Review
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Computer vision is gradually making the transition from image understanding to video understanding. This is due to the enormous success in analyzing sequences of images that has been achieved in recent years. The main shift in the paradigm has been from recognition followed by reconstruction (shape from X) to motion-based recognition. Since most videos are about people, this work has focused on the analysis of human motion. In this paper, I present my perspective on understanding human behavior.Automatically understanding human behavior from motion imagery involves extraction of relevant visual information from a video sequence, representation of that information in a suitable form, and interpretation of visual information for the purpose of recognition and learning about human behavior.Significant progress has been made in human tracking over the last few years. As compared with tracking, not much progress has been made in understanding human behavior, and the issue of representation has largely been ignored. I present my opinion on possible reasons and hurdles for slower progress in understanding human behavior, briefly present our work in tracking, representation, and recognition, and comment on the next steps in all three areas.