Browsing by Author "Syamal, Soujanya"
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Item Open Access Enhancing automotive safety through advanced object behaviour tracking for intelligent traffic and transport system(IEEE, 2024-06-26) Saha, Chandni; Tran, Trung Hieu; Syamal, SoujanyaIn the ever-evolving landscape of vehicle motion analysis, the imperative for enhanced road safety has underscored the importance of tracking object behavior, with a particular focus on vehicles. This paper proposes an innovative approach specifically designed for tracking vehicle behavior, emphasizing collision risk analysis. Central to this approach is the development of a powerful model for meticulous vehicle detection and classification, using real-world video feeds. By leveraging the YOLO algorithm, our method achieves real-time object detection, which is crucial for effective traffic monitoring. We extend our work beyond simple detection to include trajectory tracking, wherein we analyze the complexities of vehicle movement to identify patterns of traffic behavior and potential congestion hotspots. To refine our system further, we have integrated the DeepSORT algorithm, which applies the Kalman Filter and Hungarian algorithm to achieve enhanced multi-object tracking. This allows for seamless tracking through occlusions and at intersections. Our system is adept at identifying potential collision risks by employing advanced risk analysis techniques that assess severity and predict possible incidents. This paves the way for robust preventative measures and underscores our commitment to improving road safety, reducing accidents, saving lives, and enhancing traffic flow. As urban environments grow, such technological advancements are poised to make a significant impact on traffic management and safety standards. We have validated our system's performance using comprehensive datasets, showcasing marked improvements in detection accuracy, precision, and tracking capabilities under various conditions. The development and successful validation of our system not only confirm the viability of our approach but also lay the foundation for future developments in object-tracking technology for autonomous systems.Item Open Access Enhancing object detection and localization through multi-sensor fusion for smart city infrastructure(IEEE, 2024-06-26) Syamal, Soujanya; Huang, Cheng; Petrunin, IvanThe rapid advancement in autonomous systems and smart city infrastructure demands sophisticated object detection and localization capabilities to ensure safety, efficiency, and reliability. Traditional single sensor approaches often fall short, especially under complex environmental conditions. This paper introduces the CLR-Localiser, a novel multi-sensor fusion framework that synergistically integrates data from cameras, LiDAR and radar sensors mounted on roadside infrastructure to enhance object detection and 3D localization. Leveraging the complementary strengths of each sensor type, the CLR-Localiser employs an early fusion approach and deep learning techniques, including convolutional neural networks for object detection and regression networks for precise localization. We rigorously validated the performance of the CLR-Localiser against the benchmark Kitti dataset, and a custom dataset specifically designed for this research, demonstrating significant improvements in detection accuracy, localization precision, and object-tracking capabilities under diverse conditions. Our findings highlight the CLR-Localiser's potential to overcome the limitations of conventional monocular and single-sensor methods, offering a robust solution for autonomous driving, robotics, surveillance, and industrial automation applications. The development and validation of the CLR-Localiser not only prove the technical feasibility of early sensor data fusion but also pave the way for future advancements in multi-sensor fusion technology for enhanced environmental perception in autonomous systems.