Abstract
The combination of a CNN detector and a search framework forms the basis for local object/pattern detection. To handle the waste of regional information and the defective compromise between efficiency and accuracy, this paper proposes a probabilistic model with a powerful search framework. By mapping an image into a probabilistic distribution of objects, this new model gives more informative outputs with less computation. The setting and analytic traits are elaborated in this paper, followed by a series of experiments carried out on FDDB, which show that the proposed model is sound, efficient and analytic.
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Acknowledgement
This research work is funded by the National Key Research and Development Project of China (2016YFB0801003), Key Laboratory for Shanghai Integrated Information Security Management Technology Research, Science and Technology Project of State Grid Corporation of China (SGCC)
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Li, FQ., Ren, XD., Guo, HN. (2019). Probabilistic Model of Object Detection Based on Convolutional Neural Network. In: Liang, Q., Mu, J., Jia, M., Wang, W., Feng, X., Zhang, B. (eds) Communications, Signal Processing, and Systems. CSPS 2017. Lecture Notes in Electrical Engineering, vol 463. Springer, Singapore. https://doi.org/10.1007/978-981-10-6571-2_251
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DOI: https://doi.org/10.1007/978-981-10-6571-2_251
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