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Learning a dictionary of activities from motion imagery tracking data
Author(s): John M. Irvine; Richard J. Wood
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Paper Abstract

Target tracking derived from motion imagery enables automated activity analysis. In this paper, we develop methods for automatically exploiting the track data to detect and recognize activities, develop models of normal behavior, and detect departure from normalcy. We have developed methods for representing activities through syntactic analysis of the track data, by “tokenizing” the track, i.e. converting the kinematic information into strings of symbols amenable to further analysis. The syntactic analysis of target tracks is the foundation for constructing an expandable “dictionary of activities.” Through unsupervised learning on the syntactic representations, we discover the canonical activities in a corpus of motion imagery data. The probability distribution of the learned activities is the “dictionary”. Newly acquired track data is compared to the dictionary to flag atypical behaviors as departures from normalcy. We demonstrate the methods with relevant data.

Paper Details

Date Published: 27 April 2018
PDF: 8 pages
Proc. SPIE 10645, Geospatial Informatics, Motion Imagery, and Network Analytics VIII, 1064508 (27 April 2018); doi: 10.1117/12.2306006
Show Author Affiliations
John M. Irvine, Draper Lab. (United States)
Richard J. Wood, Draper Lab. (United States)


Published in SPIE Proceedings Vol. 10645:
Geospatial Informatics, Motion Imagery, and Network Analytics VIII
Kannappan Palaniappan; Peter J. Doucette; Gunasekaran Seetharaman, Editor(s)

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