Identifikasi Jumlah Manusia Dalam Kerumunan Menggunakan Convolutional Neural Network
Article Sidebar
Main Article Content
Abstract
Public space is a place that generally used by the community in order to meet their needs and where crowds are usually formed. In a crowd, the number of people can be the first indicator in an anomaly in where the more number of people exists in a crowd, the more supervision is needed on the crowd to prevent chaos or other things that are not desirable in public spaces. The need for a crowd-counting is certainly needed to facilitate supervision and also the awareness of people in crowds. This research meant to develop a system that can identifies a crowd based on the number of people that exist in the crowd and also give the number of people as an output. The system applied the Convolutional Neural Network (CNN) algorithm. The CNN model is trained using a labeled crowd dataset with a total of 4372 crowd photos. The CNN works as a regression model that will count the number of people from the feature extracted from the image. The evalution shows the Mean Absolute Error value achieved is 55.1176 in the test data.
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
This work is licensed under a Jurnal Komunikasi Creative Commons Attribution-ShareAlike 4.0 International License.
References
Fatih, Muhammad., Suciati, Nanik., Navastara, Dini A., 2021 “Deteksi Kerumunan Menggunakan Metode Fully-Convolutional Network pada kamera”,JURNAL TEKNIK ITS,Vol.10, Nomor 2, Surabaya.
Ilyas, Naveed., Shahzad, Ahsan., & Kim, Kiseon., 2019, “Convolutional-Neural Network-Based Image Crowd Counting: Review, Categorization, Analysis, and Performance Evaluation”. Sensors (Basel), Vol. 20(1), Nomor 43, Basel, Switzerland.
Kustianingrum, Dwi., Sukarya, Angga K., Nugraha, Rifan A., Rachadi, Franderdi., 2013, “Fungsi dan Aktifitas Taman Ganesha Sebagai Ruang Publik di Kota Bandung”, Jurnal Reka Karsa. Vol. 1, Nomor 2, Bandung.
Marsden, Mark., McGuinness, Kevin., Little, Suzanne., O’Conner, Noel., 2017, “Fully Convolutional Crowd Counting on Highly Congested Scenes”, Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, Vol. 5, Porto, Portugal.
Sindagi, Vishwanath A., Yasarla, Rajeev., & Patel, Vishal M., 2020, “JHU-CROWD++: Large-Scale Crowd Counting Dataset and A Benchmark Method”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 44.
Wang, Chuan., Zhang, Hua., Yang, Liang., Liu, Si., Cao, Xiaochun., 2015, “Deep People Counting in Extremely Dense Crowds”, In Proceedings of the 23rd ACM international conference on Multimedia (MM '15) - Association for Computing Machinery, New York, NY, USA.