Abstract
In urban areas, technological advancements have led to an increased focus on height as a critical human characteristic for surveillance purposes. Face recognition often encounters challenges due to occlusion and masks, necessitating the use of height, build, and torso. Accurately estimating human height in surveillance scenarios is complex due to camera calibration, posture variations, and movement patterns. This research introduces a novel human height estimation method for surveillance systems, along with a dedicated dataset. The process begins with camera calibration to rectify lens distortions. A deep learning-based YOLOv7-Occlusion Aware (YOLOv7-OA) target detection technique is employed to precisely locate individuals within the frame. The study assesses the impact of camera height and deflection angle on height estimation across different areas of the field of vision (FOV). The proposed method yields a mean absolute error of 0.02 cm to 0.8 cm across various FOV zones, surpassing the previous 1.39 cm benchmark findings.
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