Robust Detection of People in Thermal Imagery (original) (raw)
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Segmenting Humans from Mobile Thermal Infrared Imagery
2009
Perceiving the environment is crucial in any application related to mobile robotics research. In this paper, a new approach to real-time human detection through processing video captured by a thermal infrared camera mounted on the indoor autonomous mobile platform mSecurit TM is introduced. The approach starts with a phase of static analysis for the detection of human candidates through some classical image processing techniques such as image normalization and thresholding. Then, the proposal uses Lukas and Kanade optical flow without pyramids algorithm for filtering moving foreground objects from moving scene background. The results of both phases are compared to enhance the human segmentation by infrared camera. Indeed, optical flow will emphasize the foreground moving areas gotten at the initial human candidates detection.
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