Infrared target tracking via weighted correlation filter

He, Yujie; Li, Min; Zhang, Jinli; Yao, Junping

doi:10.1016/j.infrared.2015.09.010

Cited by 47 publications

(16 citation statements)

References 16 publications

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“…In Sequence 2, the background changes rapidly due to the movement of imaging platform, and a plane moves from the thick cloud region to the sky [ 37 ]. In Sequence 3, the detection barrier is noise and changing wispy clouds [ 11 ]. On a whole, the data set contains various situations in airborne infrared target detection.…”

Section: Methodsmentioning

confidence: 99%

“…To the best of our knowledge, tracklets information are rarely used in existing infrared target detection methods. Note that tracklets information are widely used in tracking problem, in which the target position in the first frame is given in advance [ 11 , 12 ]. However, there is no such prior target information in detection problem in which either a small target exists in a frame or not is still ambiguous.…”

Section: Introductionmentioning

confidence: 99%

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Robust Small Target Co-Detection from Airborne Infrared Image Sequences

Gao

Wen

Liu

2017

Sensors

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In this paper, a novel infrared target co-detection model combining the self-correlation features of backgrounds and the commonality features of targets in the spatio-temporal domain is proposed to detect small targets in a sequence of infrared images with complex backgrounds. Firstly, a dense target extraction model based on nonlinear weights is proposed, which can better suppress background of images and enhance small targets than weights of singular values. Secondly, a sparse target extraction model based on entry-wise weighted robust principal component analysis is proposed. The entry-wise weight adaptively incorporates structural prior in terms of local weighted entropy, thus, it can extract real targets accurately and suppress background clutters efficiently. Finally, the commonality of targets in the spatio-temporal domain are used to construct target refinement model for false alarms suppression and target confirmation. Since real targets could appear in both of the dense and sparse reconstruction maps of a single frame, and form trajectories after tracklet association of consecutive frames, the location correlation of the dense and sparse reconstruction maps for a single frame and tracklet association of the location correlation maps for successive frames have strong ability to discriminate between small targets and background clutters. Experimental results demonstrate that the proposed small target co-detection method can not only suppress background clutters effectively, but also detect targets accurately even if with target-like interference.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Robust Small Target Co-Detection from Airborne Infrared Image Sequences

Gao

Wen

Liu

2017

Sensors

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“…These trackers have received more attention in TIR object tracking. To deal with various challenges, a variety of the classification-based TIR trackers are presented based on sparse representation [16,18,22,23], multiple instances learning [24], kernel density estimation [25], low-rank sparse learning [26,27], structural support vector machine [28], correlation filter [17,29,30], and deep learning [19,20,31]. For instance, to address the occlusion problem, He et al [26] propose a robust low-rank sparse tracker using the low-rank constraints to capture the underlying structure of the TIR object.…”

Section: Related Workmentioning

confidence: 99%

Hierarchical spatial-aware Siamese network for thermal infrared object tracking

Liu

Fan

et al. 2019

Knowledge-Based Systems

112

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Most thermal infrared (TIR) tracking methods are discriminative, treating the tracking problem as a classification task. However, the objective of the classifier (label prediction) is not coupled to the objective of the tracker (location estimation). The classification task focuses on the betweenclass difference of the arbitrary objects, while the tracking task mainly deals with the within-class difference of the same objects. In this paper, we cast the TIR tracking problem as a similarity verification task, which is coupled well to the objective of the tracking task. We propose a TIR tracker via a Hierarchical Spatial-aware Siamese Convolutional Neural Network (CNN), named HSSNet. To obtain both spatial and semantic features of the TIR object, we design a Siamese CNN that coalesces the multiple hierarchical convolutional layers. Then, we propose a spatial-aware network to enhance the discriminative ability of the coalesced hierarchical feature. Subsequently, we train this network end to end on a large visible video detection dataset to learn the similarity between paired objects before we transfer the network into the TIR domain. Next, this pre-trained Siamese network is used to evaluate the similarity between the target template and target candidates. Finally, we locate the candidate that is most similar to the tracked target. Extensive experimental results on the benchmarks VOT-TIR 2015 and VOT-TIR 2016 show that our proposed method achieves favourable performance compared to the state-of-the-art methods.

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“…The filters are, however, pre-trained and not adaptively updated. He et al [13] employs some of the ideas of RGB-tracking methods and presents an infrared target tracking method under a tracking-by-detection framework based on a weighted correlation filter.…”

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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Infrared target tracking via weighted correlation filter

Cited by 47 publications

References 16 publications

Robust Small Target Co-Detection from Airborne Infrared Image Sequences

Robust Small Target Co-Detection from Airborne Infrared Image Sequences

Hierarchical spatial-aware Siamese network for thermal infrared object tracking

Channel Coded Distribution Field Tracking for Thermal Infrared Imagery

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