Particle filtering has been proven very successful for non-gaussian and non-linear estimation problems. In this study we
used the particle filtering technique with multiple features to track the moving object effectively in video image. The
object tracking system relies on the deterministic search of window, whose color content matches a reference histogram
model. A simple histogram-based color model is used to develop our observation system. Secondly and finally, we
describe a new approach for moving object tracking with particle filter by PCA transform technique. Our observation
system of particle filter uses the combination of color and PCA features with a likelihood measurement. Experiment
results show that the algorithm can effectively handle the effect of illumination, and is stable and robust.