Particle filter-based modulation domain infrared targets tracking

Abstract

Faced with problems of low contrast, poor SNR, and relatively complicated tracking environment, stable infrared target tracking is worth researching for its many potential applications. In this paper, instead of traditional target tracking in the pixel domain, we propose a sampling importance resampling (SIR) particle filter method with indirect velocity measurements to track infrared targets in the modulation domain. The dominant amplitude modulation (AM) features used for tracking is extracted by decomposing the input image using an 18-channel Gabor filter bank followed by the application of the dominant component analysis approach. The dominant AM modulation features provide a significant partial texture characteristic of the target which can be separated from background with better discrimination. To take advantage of observed kinematics, we utilize the augmented state vector with indirect velocity information via combining the measurements of velocity in adjacent frames to the SIR particle filter framework, which weakens weights of particles with bad velocity estimates but still having association with the cluttered background or other moving objects. A dynamic template update strategy is also provided to prevent the tracker from appearance model drift. Experiments indicate that the proposed method is effective for raising the tracking accuracy compared with other tracking methods.