Pattern Recognition and Tracking in Forward Looking Infrared Imagery

Alam, Mohammad S.

doi:10.1007/978-3-642-11568-4_4

Cited by 1 publication

(1 citation statement)

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“…Alam and Bhuiyan [1] provide an overview of the characteristics of matched filter correlation techniques for detection and tracking in TIR imagery. The filters are, however, pre-trained and not adaptively updated.…”

Section: Related Workmentioning

confidence: 99%

Channel Coded Distribution Field Tracking for Thermal Infrared Imagery

Berg

Ahlberg

Felsberg

2016

2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

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We address short-term, single-object tracking, a topic that is currently seeing fast progress for visual video, for the case of thermal infrared (TIR) imagery. The fast progress has been possible thanks to the development of new template-based tracking methods with online template updates, methods which have not been explored for TIR tracking. Instead, tracking methods used for TIR are often subject to a number of constraints, e.g., warm objects, low spatial resolution, and static camera. As TIR cameras become less noisy and get higher resolution these constraints are less relevant, and for emerging civilian applications, e.g., surveillance and automotive safety, new tracking methods are needed.Due to the special characteristics of TIR imagery, we argue that template-based trackers based on distribution fields should have an advantage over trackers based on spatial structure features. In this paper, we propose a templatebased tracking method (ABCD) designed specifically for TIR and not being restricted by any of the constraints above. In order to avoid background contamination of the object template, we propose to exploit background information for the online template update and to adaptively select the object region used for tracking. Moreover, we propose a novel method for estimating object scale change. The proposed tracker is evaluated on the VOT-TIR2015 and VOT2015 datasets using the VOT evaluation toolkit and a comparison of relative ranking of all common participating trackers in the challenges is provided. Further, the proposed tracker, ABCD, and the VOT-TIR2015 winner SRDCFir are evaluated on maritime data. Experimental results show that the ABCD tracker performs particularly well on thermal infrared sequences.

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Section: Related Workmentioning

confidence: 99%