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- W2041145807 abstract "In pedestrian detection intricate feature descriptors are used to improve the detection rate at the cost of computational complexity. In this paper, we propose a detector based on simple, robust edgelet features to enhance the detection rate and classifier based on k-means clustering approach to reduce computational complexity. The proposed framework consists of extraction of candidate features of pedestrian detection using edgelet features and use of the cascade structure of k-means clustering for classification enabling high detection accuracy at low false positives. Experimental results show that the proposed method requires less processing time per frame, making it suitable for real-time systems." @default.
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- W2041145807 date "2015-01-01" @default.
- W2041145807 modified "2023-10-04" @default.
- W2041145807 title "Vision Based Pedestrian Detection for Advanced Driver assistance" @default.
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- W2041145807 doi "https://doi.org/10.1016/j.procs.2015.02.027" @default.
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