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Conference Paper Unidirectional-Edge Detection based Background Subtraction method for Real-time Object Detection in Restricted Environments
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Authors
Whui Kim, Ju Derk Park, Jae Hong Ryu, Byeong Cheol Choi, Chang Won Lee
Issue Date
2023-10
Citation
International Conference on Information and Communication Technology Convergence (ICTC) 2023, pp.1350-1352
Language
English
Type
Conference Paper
DOI
https://dx.doi.org/10.1109/ICTC58733.2023.10393076
Project Code
23IR1100, Development of Advanced Technology for Awareness of Road Conditions based on Infrastructure Sensors, Juderk Park
Abstract
Although many studies focus on the deep learning algorithms, traditional image processing and machine learning technologies are still being developed and used as an auxiliary means. For examples, there is background subtraction based object localization. This can reduce the number of deep learning model inference. The most famous background method for subtraction is background subtraction using the GMM-derived MOG, KNN, and MOG2 algorithms. However, these algorithms still use a non-trivial of computing resources on lightweight single board computers. To alleviate this problem, we propose a unidirectional edge detection based background subtraction algorithm in restricted environments. In terms of processing time, proposed algorithm outperformed others. Although the processing time improved significantly, the precision (77.8927%) was only about 1% lower than the best method. These improvements will enable the video surveillance system to be implemented on lightweight single board computer, such as NVIDIA Jetson boards.
KSP Keywords
Background Subtraction Algorithm, Background subtraction(BS), Computing resources, Edge Detection, Image processing, Learning model, Machine learning technologies, Model Inference, Real-time object detection, Restricted environments, Single Board Computer