Development of a Real-Time Automatic Passenger Counting System using Head Detection Based on Deep Learning
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A reliable automatic passenger counting (APC) system is a key point in transportation related to the efficientscheduling and management of transport routes. In this study, we introduce a lightweight head detectionnetwork using deep learning applicable to an embedded system. Currently, object detection algorithms usingdeep learning have been found to be successful. However, these algorithms essentially need a graphicsprocessing unit (GPU) to make them performable in real-time. So, we modify a Tiny-YOLOv3 network usingcertain techniques to speed up the proposed network and to make it more accurate in a non-GPU environment. Finally, we introduce an APC system, which is performable in real-time on embedded systems, using theproposed head detection algorithm. We implement and test the proposed APC system on a Samsung ARTIK710 board. The experimental results on three public head datasets reflect the detection accuracy and efficiencyof the proposed head detection network against Tiny-YOLOv3. Moreover, to test the proposed APC system,we measured the accuracy and recognition speed by repeating 50 instances of entering and 50 instances ofexiting. These experimental results showed 99% accuracy and a 0.041-second recognition speed despite thefact that only the CPU was used.
Abstract
A reliable automatic passenger counting (APC) system is a key point in transportation related to the efficientscheduling and management of transport routes. In this study, we introduce a lightweight head detectionnetwork using deep learning applicable to an embedded system. Currently, object detection algorithms usingdeep learning have been found to be successful. However, these algorithms essentially need a graphicsprocessing unit (GPU) to make them performable in real-time. So, we modify a Tiny-YOLOv3 network usingcertain techniques to speed up the proposed network and to make it more accurate in a non-GPU environment. Finally, we introduce an APC system, which is performable in real-time on embedded systems, using theproposed head detection algorithm. We implement and test the proposed APC system on a Samsung ARTIK710 board. The experimental results on three public head datasets reflect the detection accuracy and efficiencyof the proposed head detection network against Tiny-YOLOv3. Moreover, to test the proposed APC system,we measured the accuracy and recognition speed by repeating 50 instances of entering and 50 instances ofexiting. These experimental results showed 99% accuracy and a 0.041-second recognition speed despite thefact that only the CPU was used.
참고문헌 (26)
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