Track Finding

Track finding encompasses diverse applications, from identifying particle trajectories in high-energy physics to segmenting audio recordings of test tracks in manufacturing and tracking multiple objects in dynamic environments like autonomous driving. Current research focuses on developing robust algorithms, including transformer-based models and convolutional neural networks coupled with dynamic time warping, to improve accuracy and efficiency in diverse data modalities (e.g., point clouds, audio spectrograms, image-text pairs). These advancements have significant implications for various fields, enabling more precise data analysis and improved performance in applications ranging from fundamental physics research to automated quality control and robotics.