A block matching based method for moving object detection in active camera

One of the most important challenging issues in visual surveillance systems is about detecting moving objects from video sequences captured by an active camera. In contrast to the other proposed methods which are focused on fixed cameras, approaches based on moving cameras are more complex, because making a distinction between moving object and background is difficult. Thus, detecting moving objects independently of the background from video frames is desirable. This paper introduces a new method consisting two phases. Firstly, motion vector field is extracted from output of TSS block matching algorithm. Then, it is classified into many clusters. The biggest one represents the motion direction of blocks which belong to the background and the other blocks can be regarded as moving objects. Secondly, k-means clustering algorithm segments the image to achieve the sharp boundary of moving objects. Simulation results showed that this new method can extract moving object with high accuracy and easy implementation.