In this paper, we analyze how to accurately track superpixels over extended time periods for computer vision applications. A two-step video processing pipeline dedicated to long-term superpixel tracking is proposed based on unsupervised learning and temporal integration. First, unsupervised learning-based matching provides superpixel correspondences between consecutive and distant frames using context-rich features extended from greyscale to multi-channel. Resulting elementary matches are then combined along multi-step paths running through the whole sequence with various inter-frame distances. This produces a large set of candidate long-term superpixel pairings upon which majority voting is performed. Video object tracking experiments demonstrate the efficiency of this pipeline against state-of-the-art methods.