Improved target tracking in aerial video using particle filtering

Abstract

In this paper, we present an improved target tracking algorithm in aerial video. An adaptive appearance model is incorporated in Sequential Monte Carlo framework to infer the deformation (or tracking) parameter best describing the differences between the observed appearances of the target and the appearance model. The appearance model of the target is adaptively updated based on the tracking result up to the current frame, balancing a fixed model and the dynamic model with a pre-defined forgetting parameter. For targets in the aerial video, an affine model is accurate enough to describe the transformation of the targets across frames. Particles are formed with the elements of the affine model. To accommodate the dynamics embedded in the video sequence, we employ a state space time series model, and the system noise constrains the particle coverage. Instead of directly using the affine parameters as elements of particles, each affine matrix is decomposed into two rotation angles, two scales and the translation parameter, which form the particles with more geometrical meaning. Larger variances are given to the translation parameter and the rotation angles, which greatly improve the tracking performance compared with treating these parameters equally, especially for the fast rotating targets. Experimental results show that our approach provides high performance for target tracking in aerial video.