Unsupervised detection and tracking of moving objects for video surveillance applications

Most object tracking methods applied in the video surveillance field are based on the prior pattern recognition of the moving objects. These methods are not adequate for tracking many different objects at the same time because the pattern of every moving object should be predefined. Thus, this paper introduces a new method to overcome this problem. Indeed, a new real time approach is established based on the particle filter and background subtraction. This approach is able to detect and track automatically, multiple moving objects without any learning phase or prior knowledge about the size, the nature or the initial position. An experimental study is performed over several video test sets. The obtained results show that the new method can successfully handle many complex situations. A comparison with other methods reports that the proposed approach is more advantageous in detecting objects as well as tracking them.