Robust pedestrian detection by combining visible and thermal infrared cameras.

With the development of intelligent surveillance systems, the need for accurate detection of pedestrians by cameras has increased. However, most of the previous studies use a single camera system, either a visible light or thermal camera, and their performances are affected by various factors such as shadow, illumination change, occlusion, and higher background temperatures. To overcome these problems, we propose a new method of detecting pedestrians using a dual camera system that combines visible light and thermal cameras, which are robust in various outdoor environments such as mornings, afternoons, night and rainy days. Our research is novel, compared to previous works, in the following four ways: First, we implement the dual camera system where the axes of visible light and thermal cameras are parallel in the horizontal direction. We obtain a geometric transform matrix that represents the relationship between these two camera axes. Second, two background images for visible light and thermal cameras are adaptively updated based on the pixel difference between an input thermal and pre-stored thermal background images. Third, by background subtraction of thermal image considering the temperature characteristics of background and size filtering with morphological operation, the candidates from whole image (CWI) in the thermal image is obtained. The positions of CWI (obtained by background subtraction and the procedures of shadow removal, morphological operation, size filtering, and filtering of the ratio of height to width) in the visible light image are projected on those in the thermal image by using the geometric transform matrix, and the searching regions for pedestrians are defined in the thermal image. Fourth, within these searching regions, the candidates from the searching image region (CSI) of pedestrians in the thermal image are detected. The final areas of pedestrians are located by combining the detected positions of the CWI and CSI of the thermal image based on OR operation. Experimental results showed that the average precision and recall of detecting pedestrians are 98.13% and 88.98%, respectively.

Human detection based on the generation of a background image by using a far-infrared light camera.

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.

Comparative study of human age estimation with or without preclassification of gender and facial expression.

Age estimation has many useful applications, such as age-based face classification, finding lost children, surveillance monitoring, and face recognition invariant to age progression. Among many factors affecting age estimation accuracy, gender and facial expression can have negative effects. In our research, the effects of gender and facial expression on age estimation using support vector regression (SVR) method are investigated. Our research is novel in the following four ways. First, the accuracies of age estimation using a single-level local binary pattern (LBP) and a multilevel LBP (MLBP) are compared, and MLBP shows better performance as an extractor of texture features globally. Second, we compare the accuracies of age estimation using global features extracted by MLBP, local features extracted by Gabor filtering, and the combination of the two methods. Results show that the third approach is the most accurate. Third, the accuracies of age estimation with and without preclassification of facial expression are compared and analyzed. Fourth, those with and without preclassification of gender are compared and analyzed. The experimental results show the effectiveness of gender preclassification in age estimation.

Quantitative measurement of eyestrain on 3D stereoscopic display considering the eye foveation model and edge information.

We propose a new method for measuring the degree of eyestrain on 3D stereoscopic displays using a glasses-type of eye tracking device. Our study is novel in the following four ways: first, the circular area where a user's gaze position exists is defined based on the calculated gaze position and gaze estimation error. Within this circular area, the position where edge strength is maximized can be detected, and we determine this position as the gaze position that has a higher probability of being the correct one. Based on this gaze point, the eye foveation model is defined. Second, we quantitatively evaluate the correlation between the degree of eyestrain and the causal factors of visual fatigue, such as the degree of change of stereoscopic disparity (CSD), stereoscopic disparity (SD), frame cancellation effect (FCE), and edge component (EC) of the 3D stereoscopic display using the eye foveation model. Third, by comparing the eyestrain in conventional 3D video and experimental 3D sample video, we analyze the characteristics of eyestrain according to various factors and types of 3D video. Fourth, by comparing the eyestrain with or without the compensation of eye saccades movement in 3D video, we analyze the characteristics of eyestrain according to the types of eye movements in 3D video. Experimental results show that the degree of CSD causes more eyestrain than other factors.

Finger-vein image enhancement using a fuzzy-based fusion method with Gabor and Retinex filtering.

Because of the advantages of finger-vein recognition systems such as live detection and usage as bio-cryptography systems, they can be used to authenticate individual people. However, images of finger-vein patterns are typically unclear because of light scattering by the skin, optical blurring, and motion blurring, which can degrade the performance of finger-vein recognition systems. In response to these issues, a new enhancement method for finger-vein images is proposed. Our method is novel compared with previous approaches in four respects. First, the local and global features of the vein lines of an input image are amplified using Gabor filters in four directions and Retinex filtering, respectively. Second, the means and standard deviations in the local windows of the images produced after Gabor and Retinex filtering are used as inputs for the fuzzy rule and fuzzy membership function, respectively. Third, the optimal weights required to combine the two Gabor and Retinex filtered images are determined using a defuzzification method. Fourth, the use of a fuzzy-based method means that image enhancement does not require additional training data to determine the optimal weights. Experimental results using two finger-vein databases showed that the proposed method enhanced the accuracy of finger-vein recognition compared with previous methods.