Abstract
In the past decade, there have been many proposed techniques on human detection. Dalal and Triggs suggested Histogram of Oriented Gradient (HOG) features combined with a linear SVM to handle the task. Since then, there have been many variations of HOG-based detection introduced. They are, nevertheless, based on an assumption that the human must be in upright pose due to the limitation in geometrical variation. HOG-based human detections obviously fails in monitoring human activities in the daily life such as sleeping, lying down, falling, and squatting. This paper focuses on exploring various features based on HOG for rotation invariant human detection. The results show that square-shaped window can cover more poses but will cause a drop in performance. Moreover, some rotation-invariant techniques used in image retrieval outperform other techniques in human classification on upright pose and perform very well on various poses. This could help in neglecting the assumption of upright pose generally used.
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Kittipanya-ngam, P., Lung, E.H. (2011). HOG-Based Descriptors on Rotation Invariant Human Detection. In: Koch, R., Huang, F. (eds) Computer Vision – ACCV 2010 Workshops. ACCV 2010. Lecture Notes in Computer Science, vol 6468. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22822-3_15
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DOI: https://doi.org/10.1007/978-3-642-22822-3_15
Publisher Name: Springer, Berlin, Heidelberg
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