This paper presents an Adapting Block-Propagative Background Subtraction (ABPBGS) designed for Ultra High Definition Television (UHDTV) foreground detection. The main idea is to detect block after block along the objects in order to skip all areas of the image in which there is no moving object. This is particularly interesting for UHDTV when the objects of interest could represent not even 0.1% of the total area. From a seed block which is determined in a previous iteration, the detection will spread along an object as long as it detects a part of that object. A block history map guaranties that each block is processed only once. Moreover, only small blocks are loaded and processed, thus saving computational time and memory usage. The process of each block is independent enough to be easily parallelized. Compared to 9 state-of-the-art works, the ABPBGS achieved the best results with an average global quality score of 0.57 (1 being the maximum) on a dataset of 4K and 8K UHDTV sequences developed for this work. None of the state-of-the-art methods could process 4K videos in reasonable time while the ABPBGS has shown an average speed of 5.18fps. In comparison, 5 of the 9 state-of-the-art methods performed slower on 270p down-scale version of the same videos. The experiments have also shown that for the process an 8K UHDTV video the ABPBGS can divide the memory required by about 24 for a total of 450MB.

Moving objects or more generally foreground objects are the simplest objects in the field of computer vision after the pixel. Indeed, a moving object can be defined by 4 integers only, either two pairs of coordinates or a pair of coordinates and the size. In fixed camera scenes, moving objects (or blobs) can be extracted quite easily but the methods to produce them are not able to tell if a blob corresponds to remaining background noise, a single target or if there is an occlusion between many target which are too close together thus creating a single blob resulting from the fusion of all targets. In this paper we propose an novel method to refine moving object detection results in order to get as many blobs as targets on the scene by using a tracking system for additional information. Knowing if a blob is at proximity of a tracker allows us to remove noise blobs, keep the rest and handle occlusions when there are more than one tracker on a blob. The results show that the refinement is an efficient tool to sort good blobs from noise blobs and accurate enough to perform a tracking based on moving objects. The tracking process is a resolution free system able to reach speed such as 20 000fps even for UHDTV sequences. The refinement process itself is in real time, running at more than 2000fps in difficult situations. Different tests are presented to show the efficiency of the noise removal and the reality of the independence of the refinement tracking system from the resolution of the videos.

The vast majority of foreground detection methods require heavy hardware optimization to process in real-time standard definition videos. Indeed, those methods process the whole frame for the detection but also for the background modelling part which makes them resource-guzzlers (time, memory, etc.) unable to be applied to Ultra High Definition (UHD) videos. This paper presents a real-time background modelling method called Mixed Block Background Modelling (MBBM). It is a spatio-temporal approach which updates the background model by carefully selecting block by a linear and pseudo-random orders and update the corresponding model's block parts. The two block selection orders make sure that every block will be updated. For foreground detection purposes, the method is combined with a foreground detection designed for UHD videos such as the Adaptive Block-Propagative Background Subtraction method. Experimental results show that the proposed MBBM can process 50min. of 4K UHD videos in less than 6 hours. while other methods are estimated to take from 8 days to more than 21 years. Compared to 10 state-of-the-art foreground detection methods, the proposed MBBM shows the best quality results with an average global quality score of 0.597 (1 being the maximum) on a dataset of 4K UHDTV sequences containing various situation like illumination variation. Finally, the processing time per pixel of the MBBM is the lowest of all compared methods with an average of 3.18×10-8s.