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Human Activity Recognition - A Grand Challenge

Authors: Jake K. Aggarwal;

Human Activity Recognition - A Grand Challenge

Abstract

Motion is an important cue for the human visual system. Mobiles have always fascinated children, Zeno (circa 500 B.C.) studied moving arrows to pose a paradox, and Zeke is investigating the human brain devoted to the understanding of motion. In computer vision research, motion has played an important role for the past thirty years. A major goal of current computer vision research is to recognize and understand human motion, activities and continuous activity. Initially, we focused on tracking a single person; today we focus on tracking, recognizing and understanding interactions among several people, for example at an airport or at a subway station. Interpreting such a scene is complex, because similar configurations may have different contexts and meanings. In addition, occlusion and correspondence of body parts in an interaction present serious difficulties to understanding the activity. Prof. Aggarwal’s interest in motion started with the study of motion of rigid planar objects and it gradually progressed to the study of human motion. The current work includes the study of interactions at the gross (blob) level and at the detailed (head, torso, arms and legs) level. The two levels present different problems in terms of observation and analysis. For blob level analysis, we use a modified Hough transform called the Temporal Spatio-Velocity transform to isolate pixels with similar velocity profiles. For the detailed-level analysis, we employ a multi-target, multi-assignment strategy to track blobs in consecutive frames. An event hierarchy consisting of pose, gesture, action and interaction is used to describe human-human interaction. A methodology is developed to describe the interaction at the semantic level. The recognition of human activities will lead to a number of applications, including personal assistants, virtual reality, smart monitoring and surveillance systems, as well as motion analysis in sports, medicine and choreography. Professor Aggarwal will present analysis and results, and discuss the applications of the research.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
5
Average
Average
Average
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