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Definition
Human detection may be seen as a classification problem with two classes: human and nonhumans, in which the latter class is composed of background samples containing anything but humans. When the appearance-based human detection is employed, a large number of examples of human and nonhumans are considered to capture different poses, backgrounds, and occlusion situations through the extraction of feature descriptors so that a machine learning method can be used to classify samples as belonging to either one of the classes.
Background
Due to the large number of applications that require information regarding people’s location, such as autonomous vehicles, surveillance, and robotics, finding people in images or videos presents large interest of the community. Even though widely studied in recent years [1], the human detection problem is still a challenge due to the wide variety of poses, clothing,...
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References
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Schwartz, W.R. (2014). Appearance-Based Human Detection. In: Ikeuchi, K. (eds) Computer Vision. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-31439-6_368
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DOI: https://doi.org/10.1007/978-0-387-31439-6_368
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Publisher Name: Springer, Boston, MA
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