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Modern GPUs have evolved into fully programmable parallel stream multiprocessors. Due to the nature of the
graphic workloads, computer vision algorithms are in good position to leverage the computing power of these devices.
An interesting problem that greatly benefits from parallelism is face detection. This paper presents a highly optimized Haar-based face detector that works in real time over high definition videos. The proposed kernel operations
exploit both coarse and fine grain parallelism for performing integral image computations and filter evaluations, thus being beneficial not only for face detection but also for other computer vision techniques. Compared to previous implementations, the experiments show that our proposal achieves a sustained throughput of 35 fps under 1080p resolutions using a sliding window with step of one pixel.
CitationOro, D. [et al.]. Real-time GPU-based face detection in HD video sequences. A: IEEE International Conference on Computer Vision. "2011 IEEE International Conference on Computer Vision Workshops". Barcelona: 2011, p. 530-537.
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