## 1 Introduction  
The increase in the computational power of existing systems has led to huge investments in automated data analysis. Object tracking is one such class of algorithms that automatically locates the region of interest, possibly obscured by challenging constraints [1]. These constraints are what defines the requirements that should be considered while developing real-time robust object tracking algorithms. Earlier methods successfully tracked objects from stationary cameras using background subtraction techniques [2]. These methods, when combined with data association techniques, can track multiple objects [3]. However, in these methods, the scene structure is known in advance. [PERSON] and [PERSON] introduced a method based on meanshift (MS) estimation using gradient descent that follows a reference template/model [4]. Later [PERSON], [PERSON], and [PERSON] used MS to solve the problem of tracking [5] and ever since it has shown continuous presence in the computer vision community [6; 7; 8; 9].  
MS is a non-parametric method that determines the location of the object by measuring distance between the histograms of the target and candidate templates. This is achieved by maximizing a similarity measure, the Bhattacharyya coefficient, until it converges. The MS method is robust against partial occlusion and can adapt to size/scale variation under expensive mixture of Gaussian (MoG) [8; 10; 11]. Nevertheless, since histogram disassociates the target from the neighbourhood pixel information, it fails under the constraints such as fast object motion, clutter background, and full occlusion. This constrains the object from large displacements in consecutive frames, which as a consequence mandates some part of the object to lie in the basin of search. When the object is lost, the convergence of MS either becomes very slow or it eventually loses the object. MS also becomes slow when the size of the object increases.  
On the other hand, the statistical category treats the tracking problem as a recursive computation in state space. Kalman filters (KF) and particle filters (PF) are the most popular methods in this category. KF give optimal estimation under the assumption of a linear state transition and Gaussian measurement and process noise [12]. PF, a popular statistical approach, waives off the restrictive hypothesis of KF and estimates the posterior density by combining random samples associated with weights [13; 14; 15; 16; 17; 18; 19; 20; 21]. [PERSON] et al. [16], [PERSON] et al. [18] and [PERSON] et al. [19], are some of the most prominent research that has used color histograms with the PF method for object tracking. In [16], a method was developed that uses color with the PF method and the results are compared with the MS method and MS in combination with KF. The color-based PF method showed good performance against occlusion and non-linear object motion. The method of [18] used color and shape to represent the target. However, the method was successful in multi-object tracking, but the tracker distracts from the target when multiple people cross it. Most of these methods use global histogram for object representation and in the process the spatial information is lost. [PERSON] et al. introduced a technique that models multi-part of the target to compensate for some of the spatial information [19]. The method is robust against clutter background, size/scale variation and occlusion [19]. All these research work has proved robustness and accuracy of the PF method over MS and KF, however, the accuracy is dependent on a large number of particles. This dependency is overcome through embedding the MS optimization into the PF framework and that give rise to a new class of hybrid object tracking algorithms which will be discussed next.