## 4 Experimental Results
### Visual Tracking Result  
We start with the WalkByShop1 cor dataset which is a long video sequence exhibiting several constraints. The dataset exhibits multiple full occlusions in addition to similar object appearance in \begin{table}
\begin{tabular}{c l l c c} \hline \hline
**Video Sequence** & **Description** & **Characteristic** & **Frame Size** & **No. of Frames** \\ \hline WalkByShop1 cor & \begin{tabular}{c} Couple walking along a \\ corridor browsing \\ \end{tabular} & \begin{tabular}{c} Regular mild and \\ Severe occlusion \\ \end{tabular} & \(384\times 288\) & \(2359\) \\ \hline Browse4 & \begin{tabular}{c} Person moves in an area \\ with abrupt intensity change \\ \end{tabular} & \begin{tabular}{c} abrupt illumination change \\ with non-linear motion \\ \end{tabular} & \(384\times 288\) & \(1138\) \\ \hline PETS 2007 & \
\begin{tabular}{c} People walking in crowd \\ with different obstacles \\ \end{tabular} &
\begin{tabular}{c} Severe occlusion by abrupt \\ intensity variations \\ \end{tabular} & \(576\times 720\) & \(3000\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Description of video sequence. frames 350-410 and 870-930. The results of AMF-MSPF are close to the ground truth and outperforms TSHT and PFKBOT methods as shown in Figure 2. PFKBOT fails on encountering instances having a background similar to the object. The error accumulates until the PFKBOT loses the target because FP strays the particles to false mode. However, in THST, the object recovers from the occasional distraction by the background clutter. This is due to the fact that TSHT method takes into account both the spatiotemporal aspects of the object that leads the particles towards more likely modes. Under mild but regular intensity change, TSHT is quite comparable to the proposed AMF-MSPF in the WalkByShop1 cor dataset. Figure 2 highlights the visual tracking results. Since the results of AMF-MSPF and the reference methods have come in proximity, RMSE metric is evaluated for a closer look. In Table 3, the RMSE, of AMF-MSPF, THST and PFKBOT for WalkByShop1 cor dataset are 5.76, 7.76 and 30.70 respectively. Consequently, the FAR of the proposed method is the lowest, due to very few false positives i.e., 0.13 as compared to 0.24 and 0.35 for THST and PFKBOT respectively. Moreover, the F-Score of the proposed method is 0.93 as compared to 0.86, 0.79 for THST and PFKBOT. In the next experiments, we consider two more sequences from the Browse4.mpg dataset. In these sequences, the object moves in severe intensity occluded areas. The abrupt intensity change occurs  
Figure 2: WalkByShop1 cor sequence: mild but regular occlusion. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Video Sequence**} & \multicolumn{3}{c}{**RMSE**} & \multicolumn{3}{c}{**FAR**} & \multicolumn{3}{c}{**F\_SCORE**} \\ \cline{2-10}  & **PFKBOT** & **TSHT** & **Proposed** & **PFKBOT** & **TSHT** & **Proposed** & **PFKBOT** & **TSHT** & **Proposed** \\ \hline Browse4 & 55.80 & 41.30 & 9.18 & 0.49 & 0.47 & 0.17 & 0.67 & 0.71 & 0.91 \\ WalkBy-Shop1 cor & 30.70 & 7.76 & 5.76 & 0.35 & 0.24 & 0.13 & 0.79 & 0.86 & 0.93 \\ PETS-2007 & 24.70 & 15.30 & 11.12 & 0.35 & 0.27 & 0.15 & 0.71 & 0.79 & 0.91 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: RMSE, Accuracy and F-SCORE for all video sequences. in multiple instances from frames 230-410, frames 880-920 and frames 1005-1050. The experimental results show that under abrupt intensity change, the AMF-MSPF is robust and tracks the object, as highlighted in Figures 3 and 4. In Table 3, the RMSE of AMF-MSPF, TSHT, and PFKBOT is 9.18, 41.30 and 55.80 respectively. Low RMSE indicates FAR and consequently a higher F-SCORE that is 0.91 for the proposed. In the final experiment, AMF-MSPF is tested on the PETS 2007 dataset, which exhibits continuous background change due to occlusion by intensity as well as other objects.