3D Image sequence target tracking based on multi-feature adaptive correlation filtering algorithm

A multi-feature adaptive correlation filter (MFACF-3D) was proposed in the study for target tracking in 3D image sequences, aiming to address the drift problems caused by occlusion, scale changes and complex scenes. It collaboratively integrates deep guidance filtering and the eight-parameter motion model to conduct explicit 3D geometric reasoning. By fusing HOG, color and depth features, its accuracy rate reached 91.5%, the success rate was 98% (with a 300-pixel threshold), and the success rate in the presence of occlusion was 90% - more than 15% higher than traditional methods. All improvements were statistically significant (p < 0.01).