Robust Single Object Visual Tracking Using Yolo Object Detection and Adaptive Particle Filtering

Abstract

Visual tracking is a fundamental technology in computer vision, with wide-ranging applications in fields such as robotics, autonomous driving, augmented reality, and security systems. A key challenge in these domains is ensuring stable object tracking in dynamic and complex environments. Tracking objects in real-world settings presents difficulties like occlusion, lighting changes, and sudden object movements, all of which demand the development of robust algorithms that can handle such variability effectively. This research introduces a novel single object visual tracking technique that leverages the YOLO (You Only Look Once) object detection model, combined with an adaptive particle filter. By integrating YOLO's precise object detection capabilities with the continuous state estimation offered by particle filters, this approach achieves stable object tracking, even in challenging environments. At the heart of the proposed algorithm is a dynamic fusion of YOLO detection results with image feature-based particle weight updates. When YOLO successfully detects an object, the position and size data of the object are used to adjust the state and weights of the particles, resulting in improved tracking accuracy and quick recovery from events such as sudden movements or occlusions. Conversely, in situations where YOLO detection is unavailable, the algorithm seamlessly transitions to a pure particle filter mode. In this fallback mode, visual features, such as image color histograms, are used to update particle weights, ensuring continuous and robust tracking even during temporary detection failures. The object’s dynamic state is modeled using an 8-dimensional vector, which includes information about position, velocity, size, and size-change rate. This comprehensive state representation enables precise tracking of various object motions and size transformations. Additionally, a Bhattacharyya distance-based weight calculation method is employed to assess particle similarity more effectively. Experimental results demonstrate that the proposed method excels in various challenging scenarios, including those with sudden object movements, partial occlusions, and lighting variations. The algorithm consistently delivers stable tracking, underscoring its robustness and adaptability. The contributions of this research have potential applications in fields such as autonomous driving, SLAM (Simultaneous Localization and Mapping), and augmented reality. Specifically, this technique could enhance pedestrian and vehicle tracking in autonomous driving systems and improve the accuracy of dynamic object tracking and environmental mapping in SLAM. It is also likely to play a key role in real-world applications like person tracking in security systems, player motion analysis in sports, and focused tracking in medical imaging. Future research directions include expanding the method to handle multi-object tracking and optimizing its real-time performance, broadening its applicability. Additionally, further improvements in tracking accuracy and robustness may be achieved by incorporating deep learning-based feature extraction and integrating data from various sensors.