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

Abstract

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.

Fast Object Detection Using Dimensional Based Features for Public Street Environments

“…Object candidates are extracted using signal from thermal imager only or by fusing data from camera and thermal imager. Such approaches are relatively accurate; however, this solution is sensitive to lighting changes and require an expensive (in comparison to a camera) thermal imager [24][25][26][27].…”

“…In some nighttime scenes, an object and a spot produced by its lamps can be overlapped, therefore, the division of a candidate's region should be performed. The division method proposes splitting an object into parts based on horizontal and vertical region histograms [26], whereas the criteria of splitting are extent, height to width ratio and estimated contour area. The feature extraction is completed again after the division to update object's features.…”

Pedestrian Detection Based on Adaptive Selection of Visible Light or Far-Infrared Light Camera Image by Fuzzy Inference System and Convolutional Neural Network-Based Verification

“…The pedestrian detection studies that are available to date can be divided into two groups: (a) single camera-based methods (infrared or visible-light cameras) [ 6 , 10 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 ], and (b) multiple camera-based methods [ 11 , 12 , 13 , 22 , 23 , 24 ]. The former group includes the following methods: (i) adaptive boosting (AdaBoost) cascade-based method, which is widely used as the representative facial detection scheme [ 25 , 26 ], (ii) histogram of oriented gradient-support vector machine (HOG-SVM) method [ 18 ], (iii) integral HOG [ 19 ] method, whose processing speed was reported to be significantly faster than the existing HOG, (iv) neural network-based method using the receptive field approach [ 27 ] for pedestrian detection [ 20 ], and (v) methods based on background generation with FIR cameras [ 21 ]. However, these single camera-based methods have a common constraint that their detection performance degrades when their surroundings vary.…”

Feature Channel Expansion and Background Suppression as the Enhancement for Infrared Pedestrian Detection

“…Although an increasing number of theories and methods have been put forward as solutions for visible light classification and detection problems [ 7 , 8 , 9 , 10 ], those for IR imagery detection have never been proposed in a systematical manner. In general, the IR spectrum can be classified into four sub-bands, such as near-IR, short-wave IR, medium-wave IR, far-infrared [ 11 , 12 ]. Among these bands, humans are more physically visible in the far-infrared camera than in the other cameras [ 12 , 13 ].…”