In this project, we implemented a convolutional neural network for counting object instances in images. These models are able to precisely estimate the number of vehicles in a trac congestion, or to count the humans in a very crowded scene.In this homework we used Shanghaitech dataset[1] that includes nearly 1,200 images with around 330,000 accurately labeled heads. In this homework, we used Part_B of this dataset, Part B images are taken from busy streets of metropolitan areas in Shanghai.we used the CCNN[2] (Counting Convolutional Neural Network) architecture to be implemented.
[1] Y. Zhang, D. Zhou, S. Chen, S. Gao, and Y. Ma, “Single-image crowd counting via multi-column convolutional neural network,” 2016, doi: 10.1109/CVPR.2016.70.
[2] D. Oñoro-Rubio and R. J. López-Sastre, “Towards perspective-free object counting with deep learning,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 9911 LNCS, pp. 615–629, 2016, doi: 10.1007/978-3- 319-46478-7_38.