| Summary: | 碩士 === 國立東華大學 === 資訊工程學系 === 99 === Human detection is essential for many applications such as surveillance and smart car. However, detecting humans in images or videos is a challenging task because of the variable appearance and background clutter. These factors affect significantly human shape. Therefore, in recent years, people are looking for more discriminative descriptors to improve the performance of human detection.
In this thesis, a robust descriptor based on HoG and Weber’s Law is proposed. Namely, the proposed descriptor is concatenated by U-HoG and histogram of Weber’s constant. Weber’s Constant has advantages such as robust to noise and detecting edge well. Because there are a large number of weak edges in the cluttered background affecting the detection result, the proposed method uses Weber’s constant to take off the weak edges. If a pixel on the weak edge, the proposed method will ignore the pixel when computing the feature. Therefore, the proposed descriptor may inherit the advantages of Weber’s Law. From the simulation results, the proposed descriptor has better performance than other comparative methods and is more robust to Gaussian white noise than U-HoG.
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