Aiming at tracking visual objects under harsh conditions, such as partial occlusions, illumination changes, and appearance variations, this paper proposes an iterative particle filter incorporated with an adaptive region-wise linear subspace (RWLS) representation of objects.
An ideal locating framework should be able to predict and update the motion state and observation model of an object, and even track multiple objects under various conditions.
A data association algorithm is applied to maintain tracking of multiple objects under circumstances.
Experiments show that this method has a great capability to track non-rigid moving objects under globally or locally varying illumination conditions, even when light intensities change abruptly.
An object identification task and an object tracking task are combined under this architecture.
Therefore, in this paper, we address the problem of robust object tracking under blurring and introduce a novel robust visual tracking framework based on the arbitration of the AdaBoost-based detection method and the appearance-based detection method to overcome the blurring problem.
This paper has presented a MAPF for visual object tracking under complex dynamics.
Fig. 26 Comparison of different algorithms in object tracking under low contrast condition based on tracking error.
After that time, many researches present different versions of it to use in applications such as edge detection, shape modeling, and object tracking under various restrictions.
The experimental results demonstrated that, compared with the CAM-Shift, PF, VAPF, and M-PF, the proposed algorithm was effective and robust in dealing with object tracking under conditions of complex dynamics, occlusion, and affine transformation.
