Deepcounter: Using deep learning to count garbage bags
2018 IEEE 24th International Conference on Embedded and Real-Time …, 2018•ieeexplore.ieee.org
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 …
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 …
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.
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