Person Localization System Using Privacy-Preserving Sensor

Abstract:

Article Preview

A privacy-preserving sensor for person localization has been developed. In theory, the sensor can be constructed with a line sensor and cylindrical lens because only a one-dimensional brightness distribution is needed. However, a line sensor is expensive. In contrast, CMOS area sensors are low cost and are increasing in sensitivity according to recent rapid advancement in the technology. Therefore, we covered the CMOS area sensor physically so that it behaved as a line sensor, we substituted CMOS sensors for the line sensors in practice. The proposed sensor obtains a one-dimensional horizontal brightness distribution that is approximately equal to the integration value of each vertical pixel line of the two-dimensional image. It is impossible to restore the two-dimensional detail texture image from one-dimensional brightness distribution, although it obtains enough information to detect a person’s position and movement status. Thus, the privacy is protected. Moreover, the appearance of the proposed sensor is very different from the conventional video camera, so the psychological resistance of having a picture taken is reduced. In this work, we made the privacy preserving sensor practically, and verified whether a person’s state was able to be detected. The simulation results show that the proposed sensor can detect a present person’s state responsively without violating privacy.

Info:

Periodical:

Edited by:

Qiancheng Zhao

Pages:

622-627

DOI:

10.4028/www.scientific.net/AMM.103.622

Citation:

S. Nakashima et al., "Person Localization System Using Privacy-Preserving Sensor", Applied Mechanics and Materials, Vol. 103, pp. 622-627, 2012

Online since:

September 2011

Export:

Price:

$35.00

[1] I. Kitahara, K. Kogure, and N. Hagita: Stealth Vision: A Method for Video Capturing System with Protecting Privacyi, Technical Report of IEICE, Vol. 103, No. 738 (2004), pp.89-94.

[2] T. Koshimizu, T. Toriyama, S. Nishino, and N. Babaguchi, N. Hagita: Visual Abstraction for Privacy Preserving Video Surveillance, IEICE Technical Report, Vol. 105, No. 674 (2005), pp.259-302.

[3] K. Yabuta, H. Kitazawa, and T. Tanaka: A fixed monitoring camera image processing method satisfying both privacy protection and object recognition, IEICE Technical Report, Vol. 105, No. 29 (2005), pp.13-18.

[4] T. Takenouchi, M. Morimoto, H. Kawahara, M. Takahashi, and H. Yokota: High-Reliability and Compact Passive Infrared Detectors Monitoring Two Independent Areas, Matsushita Elect. Works Tech. Rep., Vol. 52, No. 4 (2004), pp.62-68.

[5] S. Mitsutoshi, Y. Namba, and S. Serikawa: Extraction of Values Representing Human Features in Order to Develop a Privacy-preserving Sensor, International Journal of ICIC, Vol. 4, No. 4 (2008), pp.883-895.

[6] M. Hagiwara : Neuro–Fuzzy Genetic Algorithm, Sangyo Tosho (1994).

[7] S. Serikawa and T. Simomura: Proposal of a system of function–discovery using a bug type of artifical life, IEEJ Trans. on Electronics, Information and Systems, Vol. 118–C, No. 2 (1998), p.170–179.

DOI: 10.1541/ieejeiss1987.118.2_170

[8] S. Serikawa, B. B. Chhetri, and T. Simomura: Improvement of the search ability of S–System (AFunction–Discovery System), IEEJ Trans. on Electronics, Information and Systems, Vol. 120–C, No. 8/9 (2000), p.1281–1282.

DOI: 10.1541/ieejeiss1987.120.8-9_1281

[9] J. Shibata, K. Okuhara, and H. Ishii: Adaptive worker's arrangement and workload control for projectmanagement by genetic algorithm, International Journal of Innovative Computing, Information andControl, Vol. 3, No. 1 (2007), p.175–188.

[10] J. Koza: Genetic Programming, Auto Discovery of Reusable Subprograms, MIT Press (1994).

In order to see related information, you need to Login.