Title:	AN IMAGE FEATURE DESCRIPTORS-BASED RECOVERY ACTIVATION METRIC FOR FLIR TARGET TRACKING
Author(s):	Gianluca Paravati, Andrea Sanna, Fabrizio Lamberti
ISBN:	978-972-8939-48-9
Editors:	Yingcai Xiao
Year:	2011
Edition:	Single
Keywords:	Target tracking; Forward Looking Infrared (FLIR) imagery; Intensity Variation Function (IVF); Template Matching (TM); Image-based Features; Harris Corner D
Type:	Full Paper
First Page:	67
Last Page:	74
Language:	English
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Paper Abstract:	The reliability of image processing algorithms is crucial in target tracking applications where objects need to be identified along a video sequence. In general, the effectiveness of target tracking algorithms is evaluated in terms of number of target losses. Several algorithms that are triggered when the tracking phase fails exist. The challenge in the considered research area is to find a generic recovery activation metric that does not need to be finely tuned to properly work with all the video sequences. This paper describes a novel method to detect false alarms in target tracking applications based on image-based feature descriptors. The number of matches between the local feature descriptors in the reference target and in the candidate target is used to assess the reliability of the tracking algorithm used and, in case, to activate a suitable recovery mechanism. The proposed approach has been evaluated on a series of Forward Looking Infrared (FLIR) sequences provided by the U.S. Army Aviation and Missile Command (AMCOM).
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