Crowd Counting in Public Places Using MultiScale Convolutional Neural Network

Crowd of people in public places is a serious problem that needs attention because uncontrolled crowd conditions will cause problems, especially with the Covidl9 pandemic which requires people not to congregate. This research uses the Multi Scale Convolutional Neural Network method to overcome the main problems in crowd images, namely object scale variations, difficulty distinguishing between people objects and the background, as well as overlapping between people objects. The Multi Scale CNN implementation in this research uses the feature extractor layer from VGG16 as the low level feature extractor layer (frontend layer) and the Inception-Restnet-A module from Inception-Resnet-v2 as the high level feature extractor (backend layer). The datasets used to train the model are the ShanghaiTech and UCF_QNRF datasets which already contain the location information of the people in the image. Prior to the training process, ground-truth was made by conducting a convolution process using a Gaussian filter at the point where people are. Then, the Multi Scale CNN model will be trained with these 2 datasets. In the trained model, the input image will be convoluted to produce a density map. The results of the crowd calculation are obtained by adding up all the density map values. The use of Multi Scale CNN is proven to provide a good accuracy value with the MAE loss value being 78.0 for the ShanghaiTech Part A dataset and 10.75 for the ShanghaiTech Part B dataset.