Automated night/day standoff detection, tracking, and identification of personnel for installation protection

Lemoff, B. E.; Martin, Robert B.; Sluch, Mikhail; Kafka, Kristopher M.; McCormick, William; Ice, Robert

doi:10.1117/12.2016342

Cited by 3 publications

(6 citation statements)

References 3 publications

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“…Because of these drawbacks, studies using NIR or SWIR cameras have been conducted [ 10 , 11 , 12 , 13 ]. NIR or SWIR cameras are robust to illumination changes and cost less than thermal cameras.…”

Section: Related Workmentioning

confidence: 99%

“…Second, the face is then detected within the ROI using the adaboost algorithm based on extended features [ 20 ]. Lemoff et al [ 12 ] conducted tracking and recognition of people at a distance during day and night using their own tactical imager for night/day extended range surveillance (TINDERS) system. Hu et al [ 13 ] used a model that combines an adaboost and the fully convolutional network (FCN) [ 21 ] to detect the face of a person sleeping at night.…”

Section: Related Workmentioning

confidence: 99%

“…To solve this problem, a near-infrared (NIR) light camera or a thermal camera has been used in most existing nighttime face detection studies [ 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 ]. In contrast to the visible-light camera, NIR and thermal cameras use infrared (IR) wavelengths; therefore, they are robust to ambient lighting changes and low intensity of illumination.…”

Section: Introductionmentioning

confidence: 99%

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Face Detection in Nighttime Images Using Visible-Light Camera Sensors with Two-Step Faster Region-Based Convolutional Neural Network

Cho

Baek

Kim

et al. 2018

Sensors

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Conventional nighttime face detection studies mostly use near-infrared (NIR) light cameras or thermal cameras, which are robust to environmental illumination variation and low illumination. However, for the NIR camera, it is difficult to adjust the intensity and angle of the additional NIR illuminator according to its distance from an object. As for the thermal camera, it is expensive to use as a surveillance camera. For these reasons, we propose a nighttime face detection method based on deep learning using a single visible-light camera. In a long-distance night image, it is difficult to detect faces directly from the entire image due to noise and image blur. Therefore, we propose Two-Step Faster region-based convolutional neural network (R-CNN) based on the image preprocessed by histogram equalization (HE). As a two-step scheme, our method sequentially performs the detectors of body and face areas, and locates the face inside a limited body area. By using our two-step method, the processing time by Faster R-CNN can be reduced while maintaining the accuracy of face detection by Faster R-CNN. Using a self-constructed database called Dongguk Nighttime Face Detection database (DNFD-DB1) and an open database of Fudan University, we proved that the proposed method performs better compared to other existing face detectors. In addition, the proposed Two-Step Faster R-CNN outperformed single Faster R-CNN and our method with HE showed higher accuracies than those without our preprocessing in nighttime face detection.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Face Detection in Nighttime Images Using Visible-Light Camera Sensors with Two-Step Faster Region-Based Convolutional Neural Network

Cho

Baek

Kim

et al. 2018

Sensors

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“…These results have been used both in-house and shared with partners for system optimization. Conference presentations have been made to share the knowledge gained by this research effort 3,4,5,6,7 .…”

Section: Completed Tasksmentioning

confidence: 99%

“…This experimental setup consists of an OEQuest swept wavelength laser 22 and a Manlight amplifier [6]. Since both the source and the amplifier are single mode fiber devices, they were passed through a mode conditioning patch cord.…”

Section: Control Of Temporal Coherencementioning

confidence: 99%

Evaluation of the Effectiveness of the International Index of Erectile Function (IIEF-5) (SHIM) Questionnaire on the Diagnosis and Treatment of Erectile Dysfunction

Martin¹

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The capability to detect, observe, and positively identify people at a distance is important to numerous security and defense applications. Traditional solutions for human detection and observation include long-range visible imagers for daytime and thermal infrared imagers for night-time use. Positive identification, through computer face recognition, requires facial imagery that can be repeatably matched to a database of visible spectrum facial mug shots. Nighttime identification at large distances is not possible with visible imagers due to lack of light, or with thermal infrared imagers due to poor correlation with visible facial imagery. An active-SWIR imaging system was developed that is eye-safe, invisible, and capable of producing close-up facial imagery at distances of several hundred meters and full body images to thousands of meters, even in total darkness. The SWIR images correlate well to the visible spectrum allowing for biometric facial recognition and long range target detection either day or night. The process of researching existing technologies, establishing requirements of a suitable system, design process, and hardware implementation will be discussed in great detail. Once the system is assembled, it will be used to acquire data not yet produced by any existing technology. The results will be examined and a discussion made into future work.

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Fusion-based approach for long-range night-time facial recognition

et al. 2014

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Automated night/day standoff detection, tracking, and identification of personnel for installation protection

Cited by 3 publications

References 3 publications

Face Detection in Nighttime Images Using Visible-Light Camera Sensors with Two-Step Faster Region-Based Convolutional Neural Network

Face Detection in Nighttime Images Using Visible-Light Camera Sensors with Two-Step Faster Region-Based Convolutional Neural Network

Evaluation of the Effectiveness of the International Index of Erectile Function (IIEF-5) (SHIM) Questionnaire on the Diagnosis and Treatment of Erectile Dysfunction

Fusion-based approach for long-range night-time facial recognition

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