Addressing crowd control and safety at large-scale events is the central focus of this study. The proposed methodology is tested on ShanghaiTechA, ShanghaiTechB and UCF CC50 datasets. Apart from VGG-16 referred as the baseline model, the study utilizes a Convolutional Neural Network (CNN) model like VGG with dilatable layers and Atrous Spatial Pyramid Pooling (ASPP) layers on these datasets to identify every individual in the crowd by their heads. Furthermore, optical flow analysis identifies fast-moving pixels, facilitating the detection of rapid movements within the crowd. YOLO tracking is additionally employed to monitor the direction of object movement within the crowd. By integrating these methodologies, the study aims to enhance overall safety and security of individuals in the crowd. VGG with dilatable layers gives the least Mean Absolute Error for ShanghaiTechA and ShanghaiTechB datasets. The ASPP approach demonstrates approximately 15% higher accuracy on average compared to the baseline model for the ShanghaiTechA and UCF CC 50 datasets.