Development of Street Lighting System with Object Detection

Streetlight that emits too much light or shines when and where it’s not needed is wasteful. Wasting energy has huge economic and environmental consequences. Automated street light management system is needed to resolve this problem. This study aims to develop an energy management methodology applied in the streetlights of a school campus. The study is a street lighting that has the dimming capability to minimize the cost of energy consumption. The LED light will illuminate when an object is detected. Raspberry Pi and Pi camera module were used to control the dimming of the LED lights. Furthermore, it has the capability of detecting objects like people walking in the streets. The object detection was made possible in identifying human from other objects using computer vision technique. Dimming capability of streetlight has proven that it can minimize power consumption of electricity.

[1]  Jiao Feng,et al.  Intelligent streetlight energy-saving system based on LonWorks power line communication technology , 2011, 2011 4th International Conference on Electric Utility Deregulation and Restructuring and Power Technologies (DRPT).

[2]  Klamer Schutte,et al.  Likelihood-based object tracking using color histograms and EM , 2002 .

[3]  Paul A. Viola,et al.  Rapid object detection using a boosted cascade of simple features , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[4]  M. A. H. Akhand,et al.  Improvement of haar feature based face detection incorporating human skin color analysis , 2016, 2016 International Conference on Medical Engineering, Health Informatics and Technology (MediTec).

[5]  M. A. Dalla Costa,et al.  Autonomous street lighting system based on solar energy and LEDs , 2010, 2010 IEEE International Conference on Industrial Technology.

[6]  Ayra Panganiban,et al.  Wavelet-based Feature Extraction Algorithm for an Iris Recognition System , 2011, J. Inf. Process. Syst..

[7]  Michael Harville,et al.  A Framework for High-Level Feedback to Adaptive, Per-Pixel, Mixture-of-Gaussian Background Models , 2002, ECCV.

[8]  Xiaojie Wang,et al.  Human detection and object tracking based on Histograms of Oriented Gradients , 2013, 2013 Ninth International Conference on Natural Computation (ICNC).

[9]  Heimo Zeilinger,et al.  Intelligent streetlight management in a smart city , 2014, Proceedings of the 2014 IEEE Emerging Technology and Factory Automation (ETFA).

[10]  Issa Batarseh,et al.  Efficient energy solutions: Enabling smart city deployment , 2016, 2016 Future Technologies Conference (FTC).

[11]  Dong Han,et al.  Lighting-Enabled Smart City Applications and Ecosystems based on the IoT , 2015, 2015 IEEE 2nd World Forum on Internet of Things (WF-IoT).

[12]  Shivam Arora,et al.  Solar Led Street Lights using Ultrasonic Sensor , 2017 .

[13]  Yoav Freund,et al.  A decision-theoretic generalization of on-line learning and an application to boosting , 1995, EuroCOLT.