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Human Posture Detection in Always-on Body Worn Cameras

J. Preethi and V. Keerthana
Computer Science and Engineering, Anna University, Regional Campus, Coimbatore, India
Abstract—Internet of Things (IoTs) is the emerging Technology as the third wave in the development of the internet. IOT is accepted to have massive impact sensors, surveillance devices; wearable’s and body area networks with advanced interface technique. This leads to the need for a hardware-software co-design approach. We present a highly parameterized hardware design allowing configurability and the ability to evaluate different design choices in a short amount of time. This paper aims to analyze the use of Deep learning algorithm for building such reconfigurable low power devices. We develop the hardware architecture for such a system and provide experimental results obtained for the case study of Posture detection for body worn cameras used for law enforcement. Our proposed system, implemented on a Raspberry picamera platform uses minimal power consumption with an accuracy of more than 90% for the selected case study of Posture Detection. 
 
Index Terms—IOT, deep learning algorithm, human posture, picamera

Cite: J. Preethi and V. Keerthana, "Human Posture Detection in Always-on Body Worn Cameras," International Journal of Signal Processing Systems, Vol. 5, No. 1, pp. 24-27, March 2017. doi: 10.18178/ijsps.5.1.24-27
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