Convolutional neural networks (CNN) are a type of neural network designed for image classification.
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Supervised machine learning uses labeled data to train models for classification or regression over a set of targets. The performance of a model is a function of the data that is used to train it. The less data that is available, the harder it is for a model to learn to make accurate predictions on unseen data.
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Convolution Neural Networks (CNN) learns image regognition the way human visual system does. It scans images by using filters which recognizes a unique feature. A little deeper layers identify low level features such as curves and edges, while the deeper layers idtentifies high level features such as eyes or windows. We use Keras library to visualize what CNN are learning to look when making a certain classfication.
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In the light of problems caused due to poor crowd management, such as crowd crushes and blockages, there is an increasing need for computational models which can analyze highly dense crowds using video feeds from surveillance cameras. Crowd counting is a crucial component of such an automated crowd analysis system. This involves estimating the number of people in the crowd, as well as the distribution of the crowd density over the entire area of the gathering. Identifying regions with crowd density above the safety limit can help in issuing prior warnings and can prevent potential crowd crushes. Estimating the crowd count also helps in quantifying the significance of the event and better handling of logistics and infrastructure for the gathering.
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