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Development of a Real-Time Automatic Passenger Counting System using Head Detection Based on Deep Learning

Development of a Real-Time Automatic Passenger Counting System using Head Detection Based on Deep Learning
Kim, HyundukSohn, Myoung-KyuLee, Sang-Heon
Issued Date
Journal of Information Processing Systems, v.18, no.3, pp.428 - 442
Author Keywords
Automatic Passenger CountingDeep LearningEmbedded SystemHead Detection
A reliable automatic passenger counting (APC) system is a key point in transportation related to the efficient scheduling and management of transport routes. In this study, we introduce a lightweight head detection network using deep learning applicable to an embedded system. Currently, object detection algorithms using deep learning have been found to be successful. However, these algorithms essentially need a graphics processing unit (GPU) to make them performable in real-time. So, we modify a Tiny-YOLOv3 network using certain 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 the proposed head detection algorithm. We implement and test the proposed APC system on a Samsung ARTIK 710 board. The experimental results on three public head datasets reflect the detection accuracy and efficiency of 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 of exiting. These experimental results showed 99% accuracy and a 0.041-second recognition speed despite the fact that only the CPU was used © 2022 KIPS
Korea Information Processing Society
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Appears in Collections:
Division of Automotive Technology 1. Journal Articles


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