Detection of small moving objects using a moving camera

In recent years, various background subtraction methods have been proposed and used in vision systems for moving object detection and tracking from moving cameras; however, most of them have difficulty in handling small and distant objects in complicated non-flat scenes. This paper presents a robust method to effectively segment moving objects from videos, captured by a camera on a moving platform. In our approach, a two-level registration is applied to estimate the effect of camera motion for motion compensation. After motion estimation and extraction of potential foreground pixels by Gaussian mixture model, noisy result is refined using component based and pixel based methods the latter of which uses the hidden markov model (HMM) for classifying pixels. Finally, foreground objects are tracked by a particle filter to exploit the temporal coherence of foreground motion and improve the detection accuracy through time. Experimental results show that our method outperforms competing methods for detecting moving objects in complex environments.