An effective method for human detection using far-infrared images
Di Wu, Jihong Wang, Wei Liu, Jianzhong Cao, Zuofeng Zhou · 2017
In this paper, a robust real-time approach to detect humans in far-infrared images is proposed. Adaptive thresholds and vertical edge operator are combined to extract human candidate regions. Then, disturbing components are removed using morphological operations, size filtering and component labeling. After analyzing each connected region through histogram evaluation, local thresholds are employed to separate overlapped human candidates into single ones. At last, nonhuman objects are eliminated by shape refinement. Experimental results demonstrate the approach is accurate to locate human regions and efficient to meet the real-time demand of a general surveillance system.