OBJECT COUNTING PADA DATA VIDEO
Abstract
One object counting implementation is counting the number of road users from video data sources obtained from CCTV streaming. Video processing on CCTV is usually done on the server side by sending video data. If the need is only to determine the density of traffic, then the method is considered too expensive to be implemented because of the cost of internet connection and bandwidth that must be spent. The solution is to use a small computing device that can process the video first, and the calculation results are sent to the server regularly. In this study, a comparison between the Tensorflow Object Counting learning algorithm and the MOG2 Background Subtractor image processing algorithm with the aim to determine the accuracy of the calculation. The result is known that better accuracy is given by the MOG2 Background Subtractor technique and also the process is carried out using only a small percentage of the amount of memory and processor compared to the Tensorflow Object Counting technique. MOG2 Background Substractor technique is expected to be used on devices that have small data sources
Keywords : Object Counting, Tensorflow, MOG2 Background Substractor
Salah satu implementasi object counting adalah menghitung jumlah pengguna jalan dari sumber data video yang didapat dari streaming CCTV. Pemprosesan video pada CCTV biasanya dilakukan disisi server dengan mengirimkan data video. Jika keperluannya hanya untuk mengetahui kepadatan lalu lintas, maka cara tersebut dinilai terlalu mahal untuk diimplementasikan karena biaya koneksi internet dan bandwidth yang harus dikeluarkan. Pemecahannya adalah menggunakan perangkat komputasi kecil yang dapat memproses video tersebut terlebih dahulu, dan hasil perhitungannya dikirimkan ke server secara berkala. Pada penelitian ini dilakukan perbandingan antara algoritma pembelajaran Tensorflow Object Counting dan algoritma image processing MOG2 Background Substractor dengan tujuan untuk mengetahui akurasi penghitungan. Hasilnya diketahui akurasi yang lebih baik diberikan oleh teknik MOG2 Background Substractor dan juga proses yang dilakukan hanya menggunakan prosentase jumlah memori dan prosessor yang kecil dibandingkan teknik Tensorflow Object Counting. Sehingga teknik MOG2 Background Substractor ini diharapkan dapat digunakan pada perangkat yang memiliki sumber data kecil.
Kata kunci : Object Counting, Tensorflow, MOG2 Background Substractor.
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Citrus, Fruit size, Machine vision, Watershed transform, & Yield mapping. (2013). Citrus Yield Mapping System in Natural Outdoor Scenes using the Watershed Transform. https://doi.org/10.13031/2013.20853
Dorj, U. O., Lee, M., & Yun, S. seok. (2017). An yield estimation in citrus orchards via fruit detection and counting using image processing. Computers and Electronics in Agriculture. https://doi.org/10.1016/j.compag.2017.05.019
Ian Goodfellow, Yoshua Bengio, A. C. (2015). Deep Learning Book. Deep Learning. https://doi.org/10.1016/B978-0-12-391420-0.09987-X
Kang, B., & Choo, H. (2016). A deep-learning-based emergency alert system. ICT Express. https://doi.org/10.1016/j.icte.2016.05.001
Kruegle, H. (2006). CCTV Surveillance: Analog and Digital Video Practices and Technology. CCTV Surveillance.
Kurniawan, J., Dewa, C. K., & Afiahayati. (2018). Traffic Congestion Detection: Learning from CCTV Monitoring Images using Convolutional Neural Network. In Procedia Computer Science. https://doi.org/10.1016/j.procs.2018.10.530
Lecun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature. https://doi.org/10.1038/nature14539
Lehmann, E. L., & Casella, G. (1998). Theory of Point Estimation , Second Edition Springer Texts in Statistics. Design. https://doi.org/10.2307/1270597
Miao, Y., Han, J., Gao, Y., & Zhang, B. (2019). ST-CNN: Spatial-Temporal Convolutional Neural Network for crowd counting in videos. Pattern Recognition Letters. https://doi.org/10.1016/j.patrec.2019.04.012
Palaniappan Annamalai, & Won Suk Lee. (2013). Citrus Yield Mapping System Using Machine Vision. https://doi.org/10.13031/2013.13701
Schmidhuber, J. (2015). Deep Learning in neural networks: An overview. Neural Networks. https://doi.org/10.1016/j.neunet.2014.09.003
Willmott, C. J., & Matsuura, K. (2005). Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance. Climate Research. https://doi.org/10.3354/cr030079
Yu, C., Mei, Z., & Zhang, X. (2013). A real-time video fire flame and smoke detection algorithm. In Procedia Engineering. https://doi.org/10.1016/j.proeng.2013.08.140
Yusnita, R., Fariza, N., & Norazwinawati, B. (2012). Intelligent Parking Space Detection System Based on Image Processing. International Journal of Innovation, Management and Technology.
Zivkovic, Z. (2004). Improved adaptive Gaussian mixture model for background subtraction. In Proceedings - International Conference on Pattern Recognition.
Zivkovic, Z., & Van Der Heijden, F. (2006). Efficient adaptive density estimation per image pixel for the task of background subtraction. Pattern Recognition Letters. https://doi.org/10.1016/j.patrec.2005.11.005
DOI: http://dx.doi.org/10.20527/klik.v7i1.307
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