Tracking motion objects in infrared videos

We propose motion detection and object tracking method that is particularly suitable for infrared videos. Detection of moving objects in infrared videos is based on changing texture in parts of the view field. We estimate the speed of texture change by measuring the spread of texture vectors in the texture space. This method allows us to robustly detect very fast and very slow moving object. Our theoretical and experimental results show that the proposed method significantly outperforms the Stauffer-Grimson approach based on Gaussian mixture model. We observe that the proposed method does not require any post-processing, which is a necessary step for the Stauffer-Grimson approach. Moreover, the object tracking is improved when based on the spatiotemporal texture blocks.