In Tech-Fusion of infrared and visible images for robust person detection (original) (raw)
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Electronics
Pedestrian detection has important research value and practical significance. It has been used in intelligent monitoring, intelligent transportation, intelligent therapy, and automatic driving. However, in the pixel-level fusion and the feature-level fusion of visible light images and thermal infrared images under shadows during the daytime or under low illumination at night in actual surveillance, missed and false pedestrian detection always occurs. To solve this problem, an algorithm for the pedestrian detection based on the two-stage fusion of visible light images and thermal infrared images is proposed. In this algorithm, in view of the difference and complementarity of visible light images and thermal infrared images, these two types of images are subjected to pixel-level fusion and feature-level fusion according to the varying daytime conditions. In the pixel-level fusion stage, the thermal infrared image, after being brightness enhanced, is fused with the visible image. The o...
Robust Pedestrian Detection by Combining Visible and Thermal Infrared Cameras
Sensors, 2015
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
Advanced surveillance systems: combining video and thermal imagery for pedestrian detection
Thermosense XXVI, 2004
In the current context of increased surveillance and security, more sophisticated surveillance systems are needed. One idea relies on the use of pairs of video (visible spectrum) and thermal infrared (IR) cameras located around premises of interest. To automate the system, a dedicated image processing approach is required, which is described in the paper. The first step in the proposed study is to collect a database of known scenarios both indoor and outdoor with a few pedestrians. These image sequences (video and TIR) are synchronized, geometrically corrected and temperature calibrated. The next step is to develop a segmentation strategy to extract the regions of interest (ROI) corresponding to pedestrians in the images. The retained strategy exploits the motion in the sequences. Next, the ROIs are grouped from image to image separately for both video and TIR sequences before a fusion algorithm proceeds to track and detect humans. This insures a more robust performance. Finally, specific criteria of size and temperature relevant to humans are introduced as well. Results are presented for a few typical situations.
Advanced surveillance systems: combining video and thermal imagery for pedestrian detection
Proc. of SPIE, …
In the current context of increased surveillance and security, more sophisticated surveillance systems are needed. One idea relies on the use of pairs of video (visible spectrum) and thermal infrared (IR) cameras located around premises of interest. To automate the system, a dedicated image processing approach is required, which is described in the paper. The first step in the proposed study is to collect a database of known scenarios both indoor and outdoor with a few pedestrians. These image sequences (video and TIR) are synchronized, geometrically corrected and temperature calibrated. The next step is to develop a segmentation strategy to extract the regions of interest (ROI) corresponding to pedestrians in the images. The retained strategy exploits the motion in the sequences. Next, the ROIs are grouped from image to image separately for both video and TIR sequences before a fusion algorithm proceeds to track and detect humans. This insures a more robust performance. Finally, specific criteria of size and temperature relevant to humans are introduced as well. Results are presented for a few typical situations.
Pedestrian Detection by Video Processing using Thermal and Night Vision System
The paper describes the use of thermal camera and IR night vision system for the detection of Pedestrians and objects that may cause accident at night time. As per the survey most of the accidents cause is due to low vision ability of human at night time, which leads to most dangerous and higher number of accidents at night with respect to day time. This system include the IR night vision camera which detects the object with the help of IR LED and photodiode pair, this camera have capability to detect the object up to 100m. The thermal camera detects the heat generated by any of the object like cars, Human animals etc. which gives us the facility to detect the object for higher range and with low reflective surface where IR night vision may fails. With the use of these two cameras mounted on car which helps the driver to drive safely. In this system, HOG (Histogram of orientated gradients) algorithm and support vector machine (SVM) is performed with the help of OpenCV in Matlab and EmguCV in Visual Basic 2012. The system is tested on the video recorded using these cameras, and got good and efficient result. And this system is cost efficient and easy to implement.
IJERT-Detection of Human Targets from Thermal Images
International Journal of Engineering Research and Technology (IJERT), 2021
https://www.ijert.org/detection-of-human-targets-from-thermal-images https://www.ijert.org/research/detection-of-human-targets-from-thermal-images-IJERTV10IS020267.pdf The available human detection systems are used in applications like self-directed vehicles, investigate and rescue operations. These inspection systems limit itself in night surveillance due to use of RGB cameras. As we know that, there are varieties of applications like boundary surveillance, security purposes, monitoring systems, anomaly or interloper detection, which must seek a system capable of night surveillance. [1] This paper presents a human detection method for infrared images. The major contributions is the combination of the pixel-gradient and body parts processing proposed to reduce the false detection. The presented algorithm has been tested on the real thermal images using HHTI (Hand held thermal imager) taken in real environment. Also some limitations have been identified, such as problem with detection groups of the overlapped people, occluded and small targets.
Fusion of Thermal Infrared and Visible Spectrum Video for Robust Surveillance
Computer Vision, Graphics and Image Processing, 2006
This paper presents an approach of fusing the information provided by visible spectrum video with that of thermal infrared video to tackle video processing challenges such as object detection and tracking for increasing the performance and robustness of the surveillance system. An enhanced object detection strategy using gradient information along with background subtraction is implemented with efficient fusion based approach to handle typical problems in both the domains. An intelligent fusion approach using Fuzzy logic and Kalman filtering technique is proposed to track objects and obtain fused estimate according to the reliability of the sensors. Appropriate measurement parameters are identified to determine the measurement accuracy of each sensor. Experimental results are shown on some typical scenarios of detection and tracking of pedestrians.
Fusion of Range and Thermal Images for Person Detection
Detecting people in images is a challenging problem. Differences in pose, clothing and lighting, along with other factors, cause a lot of variation in their appearance. To overcome these issues, we propose a system based on fused range and thermal infrared images. These measurements show considerably less variation and provide more meaningful information. We provide a brief introduction to the sensor technology used and propose a calibration method. Several data fusion algorithms are compared and their performance is assessed on a simulated data set. The results of initial experiments on real data are analysed and the measurement errors and the challenges they present are discussed. The resulting fused data are used to efficiently detect people in a fixed camera set-up. The system is extended to include person tracking.