Estimation of Crowd Density in Surveillance Cenes Based on Deep Learning Techniques

Crowd density estimation is a significant research area in artificial intelligence applications as it is an effective tool for crowd monitoring, control, and behavior comprehension. Unstructured big data includes a substantial contribution from surveillance videos. Manual surveillance appears to be time-consuming and tedious. As a result, many CNN oriented detecting people and estimating population models were published in recent years. The enormous growth in crowd counting methods observed during the last several period is typically due to significant advancements in CNNs and datasets. Large crowd datasets and the recent advent of deep learning have made it possible for most crowd counting techniques to be remarkably successful. We have reviewed current articles on deep learning-based algorithms to recognize and categorize the regions with high crowd density in surveillance scenes to estimate the crowd density for the provided crowd scene image. This study analyzes current efforts and briefly discusses regression, detection and conventional-based density estimation methods. On video surveillance systems, several techniques mainly focus on accurately identifying and classifying crowd density. Considering a focus on using camera footage to estimate crowd volume, this overview intends to summarize studies that are crucial to the wider region of video analysis using deep learning methods. This work presents a thorough assessment along with an examination of the best current developments and efficiency improvements for assessing the density of crowds in observation settings. To ascertain the outstanding effectiveness and accuracy of the current modern techniques for deep learning population density estimation, their effectiveness is evaluated.