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Temporally stable feature clusters for maritime object tracking in visible and thermal imagery

Osborne, C., Cane, T., Nawaz, T. and Ferryman, J. (2015) Temporally stable feature clusters for maritime object tracking in visible and thermal imagery. In: 12th IEEE International Conference on Advanced Video- and Signal-based Surveillance (AVSS2015), August 25-28, 2015, Karlsruhe, Germany, pp. 1-6.

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Official URL: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?...

Abstract/Summary

This paper describes a new approach to detect and track maritime objects in real time. The approach particularly addresses the highly dynamic maritime environment, panning cameras, target scale changes, and operates on both visible and thermal imagery. Object detection is based on agglomerative clustering of temporally stable features. Object extents are first determined based on persistence of detected features and their relative separation and motion attributes. An explicit cluster merging and splitting process handles object creation and separation. Stable object clus- ters are tracked frame-to-frame. The effectiveness of the approach is demonstrated on four challenging real-world public datasets.

Item Type:Conference or Workshop Item (Paper)
Refereed:Yes
Divisions:Faculty of Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
ID Code:46525

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