Flood-survivors detection using IR imagery on an autonomous drone
نویسنده
چکیده
In the search and rescue efforts soon after disaster such as floods, the time critical activities of survivor detection and localization can be solved by using thermal long-wave infrared (LWIR) cameras which are more robust to illumination and background textures than visual cameras. This particular problem is especially challenging due to the limited computational power available on-board commercial drone platforms and the requirement of real-time detection and localization. However, the detection of humans in low resolution infrared imagery is possible due to the few hot spots that appear due to the heat signature. We propose a two-stage approach for human detection in LWIR images: (1) the application of Maximally Stable Extremal Regions (MSER) to detect hot spots instead of background subtraction or sliding window and (2) the verification of the detected hot spots using Integral Channel Features (ICF) based descriptors and a Naive Bayes classifier. The approach is validated by testing on an LWIR image dataset containing low resolution videos in real-time. The approach is novel since it achieves high detection rates while possessing a low computational runtime, and unlike several related works in human detection, without assuming that the targets are moving in the image frame.
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