Detecting Pedestrians
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Viola, Jones, and Snow recently implemented a pedestrian detection system that incorporates both appearance and motion in real-time. Simple sum-of-pixel filters are boosted into a robust pedestrian classifier. Detection is then achieved by thesholding a linear combination of these simple filters. The simplicity of the filters, along with some implementation tricks, enables the system to run in real-time. Motion information is incorporated by taking differences between successive frames in time. This paper is a reimplementation of their system, with the purpose of evaluating the merits and pitfalls of their approach. I also discuss some issues that were inadequately explained in their paper, such as how to train features used in the cascade, and provide a performance comparison.
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