Multiple human body tracking using the fusion of CCD and thermal image sensor

Kang, Byoung-Doo; Jeon, Kye-Hoon; Kyoung, Dongwuk; Kim, Seuing-Hun; Hwang, Jinyoung

doi:10.1109/aipr.2011.6176356

Cited by 3 publications

(3 citation statements)

References 12 publications

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“…This technique performed head detection with 85% accuracy. Kang et al [17] have developed a multiple moving body human detection and tracking technique with the help of CCD and thermal image sensor. They used a chess board to calibrate the CCD and thermal image that display the texture and thermal data for heterogeneous sensor fusion.…”

Section: Literature Reviewmentioning

confidence: 99%

A Deep Learning based Human Detection and Tracking for Security Surveillance Systems

Irshad

Asif

Hassan

et al. 2023

ACE

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All around the world, the crime rate has been increasing day by day, causing a rise in security issues. Closed-Circuit Television (CCTV) cameras have been installed throughout the world with the aim of decreasing crime and increasing public safety. The usage of CCTV cameras helps to increase crime detection accuracy significantly. Daily, a considerable amount of data has been recorded through CCTV cameras. Detection and recognition of culprits in the recorded data is a challenging task as it takes a lot of time, and human interaction is also involved. So, there is a need to develop a system that performs real-time detection and tracking of humans. This paper proposes a human detection and tracking system based on deep learning that assigns a unique ID to humans who enter the video scene. Multi-Task Cascaded Convolutional Neural Networks (MTCNN) and FaceNet models are used to achieve the desired target. The MTCNN model is trained on the WIDER SPACE dataset to perform human detection. FaceNet is used for human identification that is trained on the LFW dataset. The proposed system has been evaluated on 50 video sequences captured in different environments and achieved 97% average accuracy.

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Section: Literature Reviewmentioning

confidence: 99%

A Deep Learning based Human Detection and Tracking for Security Surveillance Systems

Irshad

Asif

Hassan

et al. 2023

ACE

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“…The method reaches appropriate results for human shape extraction and image registration, as well as demonstrates improvements in performance compared to none fused sensors. The two works of Kang et al 62,63 focus on a new method for human sensing by merging RGB and infrared images. The authors 62,63 use a normalized cross-correlation of average histogram algorithm to analyse the relationship between infrared and RGB data.…”

Section: Thermal Camera and Optical Cameramentioning

confidence: 99%

“…The two works of Kang et al 62,63 focus on a new method for human sensing by merging RGB and infrared images. The authors 62,63 use a normalized cross-correlation of average histogram algorithm to analyse the relationship between infrared and RGB data. Experiments show that the approach yields multiple human detection in real time.…”

Section: Thermal Camera and Optical Cameramentioning

confidence: 99%

A sensor review for human detection with robotic systems in regular and smoky environments

Gelfert

2023

International Journal of Advanced Robotic Systems

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Search and rescue robots gained a significant attention in the past, as they assist firefighters during their rescue missions. The opportunity to move autonomously or remotely controlled with intelligent sensor technology, to detect victims in unknown fire smoke environments, introduces a growing technology in fire engineering. Since sensor systems are a component of mobile robots, there is a demand for intelligent robot vision, especially for human detection in fire smoke environments. In this article, an overview of sensor technologies and their algorithms for human detection in regular and smoky environments is presented. These sensor technologies are categorized into single sensor and multi-sensor systems. Novel sensor approaches are led by artificial intelligence, 3D mapping and multi-sensor fusion. The article provides a contribution for future research directions in algorithms and applications and supports decision-makers in fire engineering to get knowledge in trends, novel applications and challenges in this field of research.

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