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.
Highlights
The need for computer vision-based human detection has increased in fields, such as security, intelligent surveillance and monitoring systems [1,2,3,4,5]
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
The infra-red (IR) spectrum can be classified into four sub-bands, such as near-IR (NIR), whose wavelength ranges from 0.75 to 1.4 μm, short-wave IR (SWIR), whose wavelength ranges from 1.4 to 3 μm, medium-wave IR (MWIR), whose wavelength ranges from 3 to 8 μm, and long-wave IR (LWIR), whose wavelength ranges from 8 to μm [6]
Summary
The need for computer vision-based human detection has increased in fields, such as security, intelligent surveillance and monitoring systems [1,2,3,4,5]. Change-based [23], EM-based [26,27] and image averaging-based [28] background modeling and subtraction. - The correct background image can be generated by image averaging, various filtering and erasing of the human area with adaptive determination of thresholds and parameters for the human detector. - Can detect object without a background image. Subtraction and Human Detection in Outdoor Videos Using Fuzzy Logic. Moving Object Detection Based on Running Average Background and Temporal Difference. In Proceedings of the International Conference on Intelligent Systems and Knowledge Engineering, Hangzhou, China, 15–16 November 2010; pp. In Proceedings of the International Conference on Intelligent Systems and Knowledge Engineering, Hangzhou, China, 15–16 November 2010; pp. 270–272
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