Real-Time Object Detection and Tracking in Traffic Surveillance: Implementing Algorithms That Can Process Video Streams for Immediate Traffic Monitoring - Vedant Singh (International Journal of Algorithms Design and Analysis Review, 2025)
The rapid growth in urban development and traffic congestion calls for adopting high standards of traffic surveillance systems for monitoring. This paper reviews the current advancement and future trends of real-time object detection and tracking technology and its implications for traffic surveillance. Conventional approaches to traffic monitoring can provide more or less accurate data, but they are not easily scalable and cannot cope with rapidly changing conditions typical within urban environments. Technical deep learning techniques like YOLO (you only look once), SSD (single shot multi-box detector), and faster R-CNN (region-based convolutional network) combined with tracking algorithms, including Kalman filters and DeepSORT, have transformed traffic management by allowing efficient real-time object detection and motion tracking. Apart from improving the identification of vehicles and pedestrians, these systems also facilitate accurate identification of instances such as accidents and congestion, facilitating quick response. This research uses hardware accelerators such as graphic processing units, edge computing, and the fifth generation of mobile communications technology for real-time processing and video resolution. Some issues, such as environmental fluctuation, obstruction, and privacy, are resolved by merging both approaches and using intelligent models. Some of the use cases include ANPR (automatic number plate recognition), pedestrian protection, and smart city, which collectively show the changes undertaken by these systems. The paper discusses traffic surveillance taking place in real-time to enhance self-driving cars and advanced technology in smart city planning and analysis. However, this paper underscores the fact that these technologies remain viable and have the potential to revolutionize mobility and transform the management of urban transport infrastructure for enhanced safety and efficiency across the world.
Keywords: Artificial Intelligence (AI); Convolutional Neural Networks (CNNs); Deep Learning; Real-time object detection; algorithms; autonomous vehicles; object tracking; traffic surveillance