Object Detection System Using K-Means Clustering
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Object Detection is one of the most popular applications in the branch of computer vision. While accuracy has always been the focus, focus has gradually also shifted to lightweight models. In this paper we propose a light weight system where object classification is not required but only object detection using clustering methods.
Kim, J. B., Park, H. S., Park, M. H., & Kim, H. J. (2002, May). Unsupervised moving object segmentation and recognition using clustering and a neural network. In Proceedings of the 2002 International Joint Conference on Neural Networks. IJCNN'02 (Cat. No. 02CH37290) (Vol. 2, pp. 12401245). IEEE.
Nguyen, H. T., Lee, E. H., Bae, C. H., & Lee, S. (2020). Multiple Object Detection Based on Clustering and Deep Learning Methods. Sensors, 20(16), 4424.
Redmon, J., & Farhadi, A. (2017). YOLO9000: better, faster, stronger. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 7263-7271).
Girshick, R. (2015). Fast r-cnn. In Proceedings of the IEEE international conference on computer vision (pp. 1440-1448).
Viola, Paul, and Michael Jones. "Rapid object detection using a boosted cascade of simple features." Proceedings of the 2001 IEEE computer society conference on computer vision and pattern recognition. CVPR 2001. Vol. 1. Ieee, 2001.
Zhao, Zhong-Qiu. "Member, Shou-tao Xu, and Xindong Wu, “Object Detection with Deep Learning: A Review”." Journal of Latex Class Files 14.8 (2017).
