DilateTrans Tracker
dong xu, leijie shao · Research Square · 2024
Abstract DilateTrans Tracker is a novel object tracking algorithm that seamlessly integrates the power of dilated attention and transformer architectures. Leveraging the strengths of both components, DilateTrans Tracker achieves robust and efficient tracking performance in complex scenarios. The dilated attention mechanism allows the model to capture long-range dependencies and spatial relationships among objects, while the transformer architecture enables effective feature extraction and global context modeling. The fusion of these two techniques results in a comprehensive solution for multi-object tracking that excels in handling diverse and dynamic scenarios. Extensive experiments demonstrate the superior performance of DilateTrans Tracker compared to state-of-the-art tracking methods, making it a promising choice for real-world applications requiring accurate and reliable object tracking.