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Title	Deepcounter: Using Deep Learning To Count Garbage Bags
ID_Doc	18122
Authors	Mikami K.; Chen Y.; Nakazawa J.; Iida Y.; Kishimoto Y.; Oya Y.
Year	2019
Published	Proceedings - 2018 IEEE 24th International Conference on Embedded and Real-Time Computing Systems and Applications, RTCSA 2018
DOI	http://dx.doi.org/10.1109/RTCSA.2018.00010
Abstract	This paper proposes DeepCounter, an automotive sensing system where deep learning based image processing technology is used to automatically count the number of collected garbage bags from the video taken by a camera mounted on the rear of a garbage truck in order to sense a fine-grain spatio-temporal distribution on the amount of disposed garbage in cities that is envisioned to be helpful to develop novel applications related to garbage collection there. A prototype system is implemented on a GPU-integrated signal-board computer. A detection-tracking-counting (DTC) algorithm is developed and implemented based on the single shot multibox detector (SSD), a well-known real-time object detection algorithm. Experimental evaluation validates the feasibility of the proposed approach using video of realistic garbage collection in Fujisawa city, Japan. © 2018 IEEE.
Author Keywords	Automotive sensing; Deep learning; Image processing; Smart cities; Urban sensing

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