The RGBT234 dataset is a comprehensive video dataset specifically designed for RGB-T (Red-Green-Blue and Thermal) tracking purposes. This dataset addresses the limitations of existing datasets like OSU-CT, LITIV, and GTOT in terms of size. RGBT234 consists of 234 RGB-T videos, each containing both an RGB video and a thermal video. The total number of frames in the dataset is approximately 234,000, with the largest video pair containing up to 8,000 frames.Each frame in the RGBT234 dataset is annotated with a minimum bounding box that covers the target for both the RGB and thermal modalities. The dataset also includes various environmental challenges such as rainy conditions, nighttime scenes, cold and hot weather scenarios. To analyze the performance of different tracking algorithms based on specific attributes, the RGBT234 dataset annotates 12 attributes and provides baseline trackers, including both deep learning and non-deep learning methods like structured SVM, sparse representation, and correlation filter-based trackers. Additionally, the dataset employs 5 metrics to evaluate the performance of RGB-T trackers effectively.