Advances in Crowd Counting and Density Estimation Using Convolutional Neural Networks
Keywords:
Convolution Neural Network, Crowed Counting, Density Estimation, PredictionAbstract
In many different applications, including urban planning, security, and event management, crowd measurement and density estimation are crucial jobs. Due to intricate spatial variations and occlusions, traditional approaches frequently encounter difficulties when dealing with a variety of crowd scenarios. CNNs have become a revolutionary tool by utilising their ability to automatically learn hierarchical characteristics from images.Numerous new developments in CNN-based crowd analysis are included. With the ability to capture information at several scales, multi-column CNN designs have proven to perform better than single-column CNN systems. The ability to concentrate on important crowd zones while reducing background noise has also improved with the inclusion of attention processes.Transfer learning techniques have made it easier for pre-trained CNNs to be modified, enabling effective crowd analysis even in situations with little labelled data. Additionally, the combination of contextual data using Graph Convolutional Networks (GCNs) and Recurrent Neural Networks (RNNs) has produced richer representations and increased accuracy.It has also become more popular to incorporate temporal information by using CNNs to process crowd image sequences, which results in predictions of crowd flow that are more precise. This is especially useful in situations where there are rapid migrations of people, like at sporting events or transportation hubs.But there are still difficulties, such as dealing with shifting lighting and viewpoints that can greatly alter crowd look. The development of CNN-based techniques that respect personal privacy is also required by the ethical implications of crowd monitoring and privacy issues.
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