Integration of Mean-Shift and Particle Filter: A Survey

Object tracking has become the cornerstone of many computer vision applications. Numerous object tracking methods have surfaced in the research community which are intended for high level applications such as automatic data analysis for activity recognition. Most of the methods are either too constrained in the context of the given application or they are costly in terms of computations to meet the real-time requirements. For example, Mean-Shift (MS) has rose to prominence due to its ease of implementation and robustness to various deformations however it fails to track objects with small sizes, fast motion and full occlusion. On the other hand, Particle Filter (PF) is known for its efficiency, accuracy and robustness to small sizes, fast object motion and full occlusion however it is heavily influenced by the number of particles, besides the sample degeneracy and impoverishment problems. Decoupling the disadvantages of both the methods gave birth to a new era of modern trackers known as hybrid systems that are more efficient, accurate and robust to the aforementioned constraints. A few survey papers on object tracking has been published in the scientific circles during the last decade however we feel that this popular integration of MS and PF is still unregistered.