Published Paper Details:

Development of an optimized spatial analysis model for crowd density estimation focuses on important limitations of scalability, accuracy, and real-time usability. By employing CNNs and furthering optimization through lightweight architectures, attention mechanisms, and knowledge distillation, the model engages in a trade-off of accuracy for a reduced computation budget. The integrative utilization of real-world and synthetic datasets adds panache and generalizability to the model across different scenarios .Using optimization techniques such as model pruning and quantization make the model deployable in edge devices and attends to real-time surveillance applications strategized for smart cities and public safety. Besides, the use of MAE and RMSE inhibitors in the testing phase provides a general sense of the model performance and certainly shows its competitiveness with other state-of-the-art methods and we have to build a new model for crowd density estimation in real time. This research gives a baseline to future innovations in crowd density estimation and gives preference to their balance in terms of accuracy, efficiency, and scalability. It validates just a few real-world applications, such as urban planning and public safety, while looking out for ethical considerations about privacy in crowd monitoring systems.