In this project the implementation of a video object tracking technique based on a particle filter that uses the partitions of the various frames in the video has been tackled. This is an extension of the standard particle filter tracker in which unions of regions of the image are used to generate particles. By doing so, the tracking of the object of interest through the video sequence is expected to be done in a more accurate and robust way. One of the main parts of this video object tracker is a co-clustering technique that allows having an initial estimation of the object in the current frame, relying on the instance of the same object in a previous frame. While developing the object tracker, we realized the importance of this co-clustering technique, not only in the context of the current video tracker but as a basic tool for several of the research projects in the image group. Therefore, we decided to concentrate on the implementation of a generic, versatile co-clustering technique instead of the simple version that was necessary for the tracking problem. This way, the main goal of this project consists on implementing the co-clustering method presented in an accurate way while presenting a low computation time. Moreover, the complete Region-based particle filter for tracking purposes is presented. Therefore, the aim of this Final Degree Project is, mainly, to give a guideline to future researchers who will use this algorithm; to help understand and apply the mentioned co-clustering for any project in need of this method.