Integrated Approach for Real-time Human Counting, Tracking, and Direction Estimation using Advanced Algorithms

Modern surveillance systems need to be able to count and track people in video streams accurately and in real time in order to support a variety of applications, from crowd control to security monitoring. This paper offers an integrated approach that makes use of the YOLOv4 and DeepSORT algorithms for accurate human detection and effective tracking, respectively, together with a method for forecasting the direction of individual movements. YOLOv4 is leveraged for its high detection accuracy and speed, ensuring that human figures are recognized rapidly in diverse environments. Subsequently, DeepSORT is used to track the identified persons between frames. This technique uses a Kalman filter in conjunction with appearance traits derived from deep learning to preserve identity consistency even in crowded settings. Furthermore, a direction estimate method is presented that provides useful contextual information regarding crowd dynamics by estimating movement directions based on the tracked individuals’ trajectories. By testing the proposed system on benchmark datasets, state-of-the-art results are obtained in terms of processing speed, accuracy, and efficiency. In addition to scalable solutions for real-time applications in regulated and urban contexts, safety and operational intelligence are improved.