## 2 Materials and Methods  
This section discusses hybrid object tracking algorithm based on embedding MS into the PF methodology (MSPF). In this technique, the MS optimization procedure replaces the expensive and inaccurate particle validation process of the PF method. The MS optimization method reduces the particle count by finding the local modes for each particle. That essentially increases the accuracy of the particles' state, which consequently reduces the need for a large number of particles. There is a volume of research that approves the accuracy and robustness of MSPF based hybrid tracking methods [22; 23; 24; 25; 26; 27; 28; 29]. [PERSON] combined PF and MS to track a hand for an application of intelligent wheelchair. Color and motion cues were used in a complementary fashion to offset the error of feature that is not discriminating the object [23]. The results of which are robust against clutter background and light illumination variation. In the same hybrid category, [PERSON] introduced a particle filter based kernel object tracking (PFKBOT) algorithm. It is based on an incremental Bhattacharyya dissimilarity into the MS optimization method [24]. The idea was to continuously distinguish the background particles from the ones lying in the target region. And, consequently, the result shows robustness to background clutter and occlusion, however, at the cost of large computational power. Likewise, color is complemented with the motion information to solve the problem of background clutter and mild illumination change [26]. A Two Stage Hybrid Tracker (TSHT) was developed that uses color histograms in combination with orientation edge histograms to include the shape and inner edges of the object [27]. The results, however, fail under occlusion but nevertheless are robust towards changes in size/scale and fast motion. TSHT uses 5 MS iterations per particle to compute probable locations, therefore it does not meet the real-time requirements. Summary of the literature review is given in Table 1. \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Ref.** & **Search Mechanism** & **Features Models** & **Robustness Towards Various Constraints** \\ \hline
[5] & & Color + Texture & Partial occlusion, clutter, and size/scale \\
[6] & & Color & Partial occlusion, size and scale \\
[7] & **Deterministic (Mean shift)** & Edge + Texture & Small objects \\
[8] & & Color & Orientation scale and position \\
[9] & & Color & Fast object motion, partial occlusion \\
[10] & & Color & Full occlusion, size and scale \\ \hline
[15] & & Color & Non-rigid deformations, partial occlusions and cluttered background \\
[16] & & Color & Clutter background, Occlusion, size/scale variation and light illumination \\
[17] & **Statistical (Particle Filters)** & Color and edge orientation & Clutter background and short time period occlusion \\
[18] & & Color and shape & Large object motion, partial occlusion \\
[19] & & Color & Cluttered background, occlusion and size/scale variation \\
[28] & & Motion model & Illumination variation and partial occlusion \\ \hline
[20] & & Color + motion Cue & Fast motion, light clutter, illumination change \\
[21] & & Color & Multiple hypothesis, clutter background \\
[22] & & HSV Color & Clutter background, size and scale \\
[23] & & Color + Motion model & Occlusion, clutter and fast motion \\
[24] & & Color + motion model & Fast object motion and clutter background \\
[25] & & Color & Background clutter, full occlusion \\
[26] & & HSV color components & Size/Scale, fast object motion, occlusion \\
[27] & & Color + Edge Orientation histogram & Size/Scale, fast motion \\
[29] & & Color + Motion model & Clutter background, light illumination variation and full occlusion \\
[30] & & Color + local integral orientation & Scale and pose variation \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of state-of-the-art hybrid object tracking algorithms.