Abstract
This article puts forward a novel framework for pedestrian detection tasks, which proposing a model with both sparse reconstruction and class discrimination components, jointly optimized during dictionary learning. We present an efficient pedestrian detection system using mixing sparse features of HOG, FOG and CSS to combine into a Kernel classifier. Results presented on our data set show competitive accuracy and robust performance of our system outperforms current state-of-the-art work.
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Cheng, K., Mao, Q., Zhan, Y. (2013). Pedestrian Detection Based on Kernel Discriminative Sparse Representation. In: Pan, Z., Cheok, A.D., Müller, W., Liarokapis, F. (eds) Transactions on Edutainment IX. Lecture Notes in Computer Science, vol 7544. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37042-7_12
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DOI: https://doi.org/10.1007/978-3-642-37042-7_12
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