## 3 Proposed System Methodology
### Crowd Behavior Detection
#### 3.4.1 Global-Local Descriptors
#### 4.2.2 Experiment 2: Human Crowd Behavior Detection over the UMN Dataset

Experimental results using the confusion matrix and the mean accuracy of our proposed HCB model on the publicly available UMN dataset are shown in Figure 13:1 The way to evaluate algorithms is to run them throughout a test sequence with initialization from the ground truth position in the first frame. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sequence No (Frame = 20)** & **Actual Count** & **Predicted Count** & **Accuracy** \\ \hline
6 & 3 & 3 & 100 \\
12 & 4 & 4 & 100 \\
18 & 5 & 4 & 80 \\
24 & 6 & 5 & 83.33 \\
30 & 7 & 6 & 85.71 \\ \hline \multicolumn{4}{c}{Mean Accuracy = 89.80\%} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Multi-person counting accuracy over the PETS2009 dataset. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sequence No (Frame = 20)** & **Successful** & **Failure** & **Accuracy** \\ \hline
6 & 3 & 0 & 100 \\
12 & 4 & 0 & 100 \\
18 & 4 & 1 & 80 \\
24 & 5 & 1 & 83.33 \\
30 & 5 & 2 & 71.43 \\ \hline \multicolumn{4}{c}{Mean Accuracy = 86.95\%} \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Multi-person tracking accuracy over PETS2009 dataset. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Events** & **Normal** & **Abnormal** \\ \hline Normal & 88 & 12 \\ \hline Abnormal & 16 & 84 \\ \hline \multicolumn{4}{c}{Mean Accuracy of Event Detection = 86.06\%} \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Confusion matrix, showing mean accuracy for human crowd behavior detection on the UMN dataset. .2.3 Experiment 3: Multi-Person Tracking and HCB Detection Comparisons with State-of-the-Art Methods

We compared our proposed system with other well-known multi-person tracking and human crowd behavior detection methods. As depicted, our system performed better compared to existing well-known state-of-the-art methods. Table 4 shows that, in comparison to other state-of-the-art methods, our proposed system achieved an admirable accuracy rate of 86.06% for crowd behavior detection, which is higher than the accuracy of the force field model (FF) (81.04%) and the social force model (SF) (85.09%). The accuracy of other methods under the same evaluation settings was taken from [77; 79]. Table 5 presents the comparison of our proposed system with other state-of-the-art systems for multi-person counting. Experiment results show that our proposed system achieved a higher accuracy rate of 89.8% over existing methods. In Table 6, comparisons of multi-person tracking with other state-of-the-art methods show that our proposed system achieved a higher accuracy rate of 86.9% over existing methods. ## 5 Conclusions

In this paper, we proposed a new robust approach for crowd counting. We introduced and tested tracking and human behavior detection using the idea of a mutually interacting particles force model and an improved entropy classifier with spatio-temporal and particles gradient motion descriptors. Through detailed experiments, we proved the ability of the method to efficiently count, track, and detect the behavior of multiple persons efficiently in crowded scenes. The performance of our new tracking system decreases marginally with increasing numbers of persons in the scene. This is mainly due to full occlusions that occur in the test videos. We achieved promising results on the publicly available benchmark PETS2009 dataset, with an accuracy of 89.80% for multi-person counting and 86.95% for person tracking, as shown in Tables 1 and 2. However, for HCB detection, we achieved \begin{table}
\begin{tabular}{c c} \hline \hline
**Methods** & **Tracking Accuracy (\%)** \\ \hline Flow Linear Programming [112] & 78.8 \\ DDPMO [113] & 81.3 \\ Appearance model [114] & 83.0 \\ Proposed method & 86.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of the proposed approach with state-of-the-art multi-person tracking methods.