Feature Enhancement Using Gradient Salience on Thermal Image

Li, Zelin; Zhang, Jian; Wu, Qiang; Geers, Glenn

doi:10.1109/dicta.2010.99

Cited by 10 publications

(11 citation statements)

References 14 publications

(25 reference statements)

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“…However, under different conditions, the characteristics of the human areas and background can change. This can influence the accuracy of detecting the precise locations and shapes of the human areas in the thermal image [ 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 ].…”

Section: Introductionmentioning

confidence: 99%

“…For the former category, Martin et al proposed a detector and tracker of objects based on motion and scale-invariant feature transform (SIFT) methods [ 2 ]. In addition, the former used the methods for feature extraction based on the histogram of the oriented gradient (HOG) [ 7 , 8 , 9 , 15 , 16 , 17 ] and geometric characteristics [ 15 ] with a classifier based on support vector machine (SVM) [ 7 , 15 , 17 , 18 ], the adaptive boosting (Adaboost) method [ 19 ] and the soft-label boosting algorithm [ 20 ]. The advantage of these methods is that they can detect an object without a background image.…”

Section: Introductionmentioning

confidence: 99%

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Human Detection Based on the Generation of a Background Image by Using a Far-Infrared Light Camera

Jeon

Choi

Lee

et al. 2015

Sensors

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The need for computer vision-based human detection has increased in fields, such as security, intelligent surveillance and monitoring systems. However, performance enhancement of human detection based on visible light cameras is limited, because of factors, such as nonuniform illumination, shadows and low external light in the evening and night. Consequently, human detection based on thermal (far-infrared light) cameras has been considered as an alternative. However, its performance is influenced by the factors, such as low image resolution, low contrast and the large noises of thermal images. It is also affected by the high temperature of backgrounds during the day. To solve these problems, we propose a new method for detecting human areas in thermal camera images. Compared to previous works, the proposed research is novel in the following four aspects. One background image is generated by median and average filtering. Additional filtering procedures based on maximum gray level, size filtering and region erasing are applied to remove the human areas from the background image. Secondly, candidate human regions in the input image are located by combining the pixel and edge difference images between the input and background images. The thresholds for the difference images are adaptively determined based on the brightness of the generated background image. Noise components are removed by component labeling, a morphological operation and size filtering. Third, detected areas that may have more than two human regions are merged or separated based on the information in the horizontal and vertical histograms of the detected area. This procedure is adaptively operated based on the brightness of the generated background image. Fourth, a further procedure for the separation and removal of the candidate human regions is performed based on the size and ratio of the height to width information of the candidate regions considering the camera viewing direction and perspective projection. Experimental results with two types of databases confirm that the proposed method outperforms other methods.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Human Detection Based on the Generation of a Background Image by Using a Far-Infrared Light Camera

Jeon

Choi

Lee

et al. 2015

Sensors

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“…Thermal cameras have been successfully implemented for pedestrian detection [12,20,26,27] with various detection methods such as HOG [20,26,23,5]; shape and appearance-based detection [6] and contour saliency map (CSM) [8].…”

Section: Thermal Infrared Sensorsmentioning

confidence: 99%

Visual and Thermal Data for Pedestrian and Cyclist Detection

Ahmed

Huda

Rajbhandari

et al. 2019

Lecture Notes in Computer Science

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With the continued advancement of autonomous vehicles and their implementation in public roads, accurate detection of vulnerable road users (VRUs) is vital for ensuring safety. To provide higher levels of safety for these VRUs, an effective detection system should be employed that can correctly identify VRUs in all types of environments (e.g. VRU appearance, crowded scenes) and conditions (e.g. fog, rain, night-time). This paper presents optimal methods of sensor fusion for pedestrian and cyclist detection using Deep Neural Networks (DNNs) for higher levels of feature abstraction. Typically, visible sensors have been utilised for this purpose. Recently, thermal sensors system or combination of visual and thermal sensors have been employed for pedestrian detection with advanced detection algorithm. DNNs have provided promising results for improving the accuracy of pedestrian and cyclist detection. This is because they are able to extract features at higher levels than typical hand-crafted detectors. Previous studies have shown that amongst the several sensor fusion techniques that exist, Halfway Fusion has provided the best results in terms of accuracy and robustness. Although sensor fusion and DNN implementation have been used for pedestrian detection, there is considerably less research undertaken for cyclist detection.

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“…The thermal cameras can here often be a better choice than a normal visual camera. The methods applied to thermal imaging span from simple thresholding and shape analysis [43,17,39,15,7] to more complex, but well-known methods such as HOG and SVM [42,37,41,31,26] as well as contour analysis [10,9,27,38]. Using simple methods allows for fast real-time processing, and combined with the illumination independency, the thermal sensor is very well suited for detecting humans in real-life applications.…”

Section: Related Workmentioning

confidence: 99%