Abstract
Researches show in the test phase of Convolutional Neural Network (CNN), the most time cost occurred in convolutional layers, while the most memory cost occurred in the fully connected layers. With the rapid development of the pedestrian methods, which are based on deep learning, the performance is going better and better. Especially using resemble models, pedestrian detection can get a more excellent performance. However, the performance is improved by the increase in parameters and slow of speed in price. Meanwhile, for some specific tasks, such as driverless cars, due to the limitations of hardware facilities, it is impossible to use these methods on them. In this paper, we applied the tensor decomposition to pedestrian detection task, in order to accelerate the whole pedestrian detection processing. Experiments show even though the decomposition brings some of the rise of miss rate (MR), the saved memory and time indicates the efficiency of our method.
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Wu, Y., Jiang, W., Li, J., Yang, T. (2017). Speeding Up Dilated Convolution Based Pedestrian Detection with Tensor Decomposition. In: Huang, DS., Hussain, A., Han, K., Gromiha, M. (eds) Intelligent Computing Methodologies. ICIC 2017. Lecture Notes in Computer Science(), vol 10363. Springer, Cham. https://doi.org/10.1007/978-3-319-63315-2_11
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DOI: https://doi.org/10.1007/978-3-319-63315-2_11
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