## 3 Proposed System Methodology
### Crowd Behavior Detection
#### 3.4.1 Global-Local Descriptors
For a small entropy value less than the defined threshold, the behavior was predicted as normal; however, for entropy values higher than the set threshold, the behavior was presumed to be abnormal. A flow chart of the proposed improved entropy classifier is shown in Figure 12:4 Figure 19 depicts results over event classes. Figure 12:5 Crowd behavior detection. (**a**,**c**) Normal frames and (**b**,**d**) abnormal frames. Figure 12:6 Flow chart of the improved entropy classifier. ## 4 Performance Evaluation

In this section, we evaluated the performance of our proposed system. We conducted experiments on two publicly available benchmark datasets to evaluate the accuracy and performance of our proposed model. The PETS2009 dataset was used to evaluate the accuracy of multi-person tracking and the UMN dataset was used to evaluate the accuracy of crowd behavior detection. We started by briefly describing the datasets used, and then the experimental results were discussed. Finally, we showed the mean accuracy of our proposed system. We also compared our proposed model with other state-of-the-art multi-person tracking and crowd behavior detection systems. ### Datasets Description

#### 4.1.1 PETS2009 Dataset

To evaluate different video surveillance challenges, we used PETS2009, one of the publicly available benchmark datasets. The challenges included the S1 dataset for counting persons in a low-density crowd, the S2 dataset for detecting and tracking persons in medium-density crowds, and the S3 dataset for tracking and estimating the number of persons in a high-density crowd. Some sample frames of different synchronized views from PETS2009 dataset are depicted in Figure 12:7

#### 4.1.2 UMN Dataset

To evaluate different video surveillance challenges for crowd behavior detection, UMN is one of the publicly available benchmark datasets. The UMN dataset consists of three different scenes, specifically, two outdoor and one indoor, with videos of 11 various panic scenarios. For the detection of abnormal behavior of a crowd, the UMN dataset is one of the best datasets that is publicly available. There were two outdoor scenes: the lawn scene, consisting of two scenarios with 1453 frames, and the Plaza scene, with three scenarios that had 2142 frames. There were six scenarios in the indoor scene, with 4144 frames. Sample frames of different scenarios of the UMN dataset are shown in Figure 12:8

### Experimental Settings and Results

We performed all the experiments on MATLAB, and the hardware system had a 64-bit intel core-i3 2.5 GHz CPU and 6 GB of RAM. Three experimental measures were used to evaluate the performance of the system: (1) mean accuracy of multi-person tracking, (2) the

Figure 12:9 Sample frames of different scenarios of the UMN dataset. Figure 13:0 Sample frames of different synchronized views from the PETS2009 dataset. accuracy of human crowd behavior detection, and (3) comparisons between our proposed new system with other current and well-known systems. Experimental results showed that our proposed system produces a higher accuracy rate over existing systems. #### 4.2.1 Experiment 1: Multi-Person Tracking over the PETS2009 Dataset

Experimental results and mean accuracy of our proposed multi-person counting and tracking model on a publicly available PETS2009 dataset are shown in Tables 1 and 2. The ground truth was obtained by counting the number of persons in every sequence, where one sequence contained 20 frames. Table 1 depicts the mean accuracy of our proposed multi-person counting system on the first 30 sequences. As shown, the mean accuracy of our proposed model was 89.80%. Table 2 presents the mean accuracy of our proposed multi-person tracking system. The actual number of humans is the same as for Table 1, while column 2 represents the successful tracking rate of our proposed particles force model and column 3 depicts the failure case. The mean accuracy of our proposed model for multiple person tracking was 86.95%.