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Erschienen in:
2015 | OriginalPaper | Buchkapitel
Hardware Architecture for Real-Time Face Detection on Embedded Analog Video Cameras
verfasst von : Mooseop Kim, Ki-Young Kim
Erschienen in: Computer Science and its Applications
Verlag: Springer Berlin Heidelberg
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This paper proposes novel hardware architecture for real-time face detection, which is efficient and suitable for embedded system. The proposed architecture is based on AdaBoost learning algorithm with Haar-like features and it aims to apply to a low-cost FPGA that can be applied to legacy analog cameras as a target platform. We propose the using of cumulative line sum to calculate integral image and an alternative method to avoid costly division for the computing of a standard deviation. The experimental results show that the processing time for a 320×240 pixel image is 42 frames per second with the 100MHz, which is about 3 times faster than previous works.