Moving Object Detection using Visible Spectrum Imaging and Thermal Imaging

Mangale, Supriya; Khambete, Madhuri

doi:10.1109/iic.2015.7150810

Cited by 7 publications

(6 citation statements)

References 13 publications

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“…18 Visible spectrum and thermal imaging are naturally complementary to one other so that they provide best data. 18 The division of movable objects in correlated frames of videos uses 'Background Subtraction' technique. 18 Visible and Thermal images are fused as…”

Section: R E T R a C T E Dmentioning

confidence: 99%

“…18 The division of movable objects in correlated frames of videos uses 'Background Subtraction' technique. 18 Visible and Thermal images are fused as…”

Section: R E T R a C T E Dmentioning

confidence: 99%

“…cluster to gaussians for the purpose of background subtraction and after that study of blob and fusion technique is used. 18 The method involved is background subtraction (BS) and combining of thermal and visible images by Gaussian mixture models (GMM). 19 Dynamic adaption of combination method is found as it works simultaneously in day/night and similarly will do the required changes.…”

Section: Teju and Bhavanamentioning

confidence: 99%

“…17 Identifying of movable objects is the major theme in this paper. 18 To know in depth about moving objects, combination of visible spectrum and thermal imaging is used. 18 Visible spectrum and thermal imaging are naturally complementary to one other so that they provide best data.…”

Section: R E T R a C T E Dmentioning

confidence: 99%

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RETRACTED: An efficient object detection using OFSA for thermal imaging

Teju

Bhavana

2020

International Journal of Electrical Engineering & Education

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The demand for identifying a reliable person is increased because of security issues in our daily life. At present, to identify a person biometric technique such as face recognition is introduced. Since,a person with abnormal behaviour recognition system has reached certain level, their accomplishments in real time applications are restricted by challenges, such as illumination variations. The present visual recognition system is good at controlled illumination conditions and thermal face recognition system is better for detecting disguised persons or when there is no illumination control. Hence, a hybrid system which uses both visual and thermal images for recognising a person is better. The objective of this research is to implement a method which improves the quality of the image by fusing visual and thermal imaging images. Our research methodology has introduced to enhance servo line camera images. Nonlinear image transfer functions were introduced,and the parameters associated with those functions are determined by image statistics for making adaptive algorithms. Next methodswereintroduced for registering the visual images to their consequent thermal images. To get a transformation matrix for the registration, the landmarks in the images are first detected and a subset of those landmarks were selected to obtain the matrix, we propose a hybrid algorithm for detection, tracking and classification using OFSA algorithm to fuse the registered thermal and visual images. In this research, we focus on object detection using OFSA algorithm for more accuracy.

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Section: R E T R a C T E Dmentioning

confidence: 99%

“…18 The division of movable objects in correlated frames of videos uses 'Background Subtraction' technique. 18 Visible and Thermal images are fused as…”

Section: R E T R a C T E Dmentioning

confidence: 99%

Section: Teju and Bhavanamentioning

confidence: 99%

Section: R E T R a C T E Dmentioning

confidence: 99%

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RETRACTED: An efficient object detection using OFSA for thermal imaging

Teju

Bhavana

2020

International Journal of Electrical Engineering & Education

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“…Detection of objects in low‐light images using thermal imagery has been an active research topic in computer vision [8–10 ] specifically in surveillance. Most of the deep learning‐based object detection networks are currently designed using single‐modal sensory data, typically RGB/visible images.…”

Section: Related Workmentioning

confidence: 99%

ABiFN: Attention‐based bi‐modal fusion network for object detection at night time

Charan¹,

Jitesh²,

Chowdhury³

et al. 2020

Electron. lett.

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Camera‐based object detection in low‐light/night‐time conditions is a fundamental problem because of insufficient lighting. So far, a mid‐level fusion of RGB and thermal images is done to complement each other's features. In this work, an attention‐based bi‐modal fusion network is proposed for a better object detection in the thermal domain by integrating a channel‐wise attention module. The experimental results show that the proposed framework improves the mAP by 4.13 points on the FLIR dataset.

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Camouflaged Target Detection and tracking using thermal infrared and visible spectrum imaging

Mangale

Khambete²

2016

Advances in Intelligent Systems and Computing

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Moving Object Detection using Visible Spectrum Imaging and Thermal Imaging

Cited by 7 publications

References 13 publications

RETRACTED: An efficient object detection using OFSA for thermal imaging

RETRACTED: An efficient object detection using OFSA for thermal imaging

ABiFN: Attention‐based bi‐modal fusion network for object detection at night time

Camouflaged Target Detection and tracking using thermal infrared and visible spectrum imaging

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