Simulation of Human Detection System Using BRIEF and Neural Network

Yuto Yasuoka; Yuki Shinomiya; Yukinobu Hoshino

doi:10.20965/jaciii.2016.p1159

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JACIII Vol.20 No.7 pp. 1159-1164

doi: 10.20965/jaciii.2016.p1159

(2016)

Paper:

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Simulation of Human Detection System Using BRIEF and Neural Network

Yuto Yasuoka, Yuki Shinomiya, and Yukinobu Hoshino

Kochi University of Technology
Kami, Kochi 782-8502, Japan

Received:

March 19, 2016

Accepted:

October 14, 2016

Published:

December 20, 2016

Keywords:

binary robust independent elementary features (BRIEF), neural network (NN), particle swarm optimization (PSO)

Abstract

Pedestrian detection systems are increasing in popularity recently. These systems that work together with car-mounted cameras need to operate in real-time. A Field Programmable Gate Array (FPGA) is able to work in a highly optimized parallel process and hence it is expected to work in real-time. However, it is difficult for FPGA to calculate complex processes. Therefore, pedestrian detection methods must have low computational costs in order to implement the system using FPGA. This paper proposes a system that uses Binary Robust Independent Elementary Features (BRIEF) and a Neural Network (NN) as a pedestrian detection method. The system was simulated using a CPU and the human detection performance was evaluated. Additionally, the NN was trained using three Particle Swarm Optimization (PSO) methods. The performance of our approach was shown using a Receiver Operating Characteristic (ROC) curve with respect to each learning method. In the future, the system needs to improve the human detection rate and it will be implemented and simulated using an FPGA.

Cite this article as:

Y. Yasuoka, Y. Shinomiya, and Y. Hoshino, “Simulation of Human Detection System Using BRIEF and Neural Network,” J. Adv. Comput. Intell. Intell. Inform., Vol.20 No.7, pp. 1159-1164, 2016.

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References

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This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.

[1] [1] J. A. Gomez-Pulido, M. A. Vega-Rodriguez, J. M. Sanchez-Perez, S. Priem-Mendes, and V. Carrieia, “Accelerating floating-point fitness functions in evolutionary algorithms: A fpga-cpu gpu performance comparison,” Genetic Programming and Evolvable Machines, Vol.12, No.4, pp. 403-427, 2011.

[2] [2] A. Bezborah, “A hardware architecture for training of artificial neural networks using particle swarm optimization,” 2012 3rd Int. Conf. on Intelligent Systems, Modelling and Simulation (ISMS), pp. 67-70, 2012.

[3] [3] M. Calonder, V. Lepetit, C. Strecha, and P. Fua, “BRIEF: Binary Robust Independent Elementary Features,” Proc. of the 11th European Conf. on Computer Vision: Part IV, pp. 778-792, 2010.

[4] [4] U. Janen, C. Paul, M. Wittke, and J. Hahner, “Multi-object tracking using feed-forward neural networks,” 2010 Int. Conf. of Soft Computing and Pattern Recognition (SoCPaR), IEEE, pp. 176-181, 2010.

[5] [5] C. Wohler, J. K. Anlauf, T. Portner, and U. Franke, “A Time Delay Neural Network Algorithm for Real-Time Pedestrian Recognition,” Int. Conf. on Intelligent Vehicle, Vol.10, pp. 247-251, 1998.

[6] [6] H. A. Rowley, “Neural Network-Based Face Detection,” IEEE Computer Society, pp. 23-38, 1998.

[7] [7] C. J. Lin and H. M. Tsai, “FPGA implementation of a wavelet neural network with particle swarm optimization learning,” Math. Computer Modelling, Vol.47, No.9-10, pp. 982-996, 2008.

[8] [8] J. Kennedy and R. Eberhart, “Particle Swarm Optimization,” Proc. IEEE Int. Conf. on Neural Networks, Perth, Australia, IEEE Service Center, 1995.

[9] [9] V. G. Fudise and G. K. Venayagamoorthy, “Comparison of particle swarm optimization and backpropagation as training algorithms for neural networks,” Proc. of Swarm Intelligence Symposium (SIS ’03), IEEE, pp. 110-117, 2003.

[10] [10] Y. Shi and R. Eberhart, “Parameter selection in particle swarm optimization,” Proc. of 7th Int. Conf., EP98 San Diego, pp. 591-600, 1998.

[11] [11] Y. Yasuoka, Y. Shinomiya, and Y. Hoshino, “Development of human detection system by BRIEF,” ISIS 2015, Korea.

[12] [12] Y. Hoshino and H. Takimoto, “PSO training of the neural network application for a controller of the line tracing car,” 2012 IEEE Int. Conf. on Fuzzy Systems FUZZ-IEEE, pp. 1-8, 2012.