## 3 Experimental Analysis and Results  
We implemented the proposed method on a PC with Intel Corei7 (Intel Corporation, Santa Clara, CA, USA), 3.6 GHz processor, and 8 GB RAM. The experiments were conducted in a Python programming environment. To verify the performance of the proposed method in detecting objects  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Intensity Level** & **Number of Pixels** & **Approximation Error** & **Intensity Level** & **Number of Pixels** & **Approximation Error** \\ \hline
72 & 1918 & 0 & 168 & 61 & 0.5647 \\
78 & 1271 & 0.2193 & 174 & 52 & 0.5634 \\
102 & 513 & 0.3933 & 180 & 23 & 0.7728 \\
108 & 279 & 0.611 & 204 & 61 & 0.1501 \\
114  
using image segmentation, we tested our method on the infrared images extracted from recently developed standard databases. The original images with no modifications were considered for the experimental analysis and the database details are provided in Table 2.  
LTIR is a more challenging dataset for thermal object detection. Thermal image sequences collected in the LTIR dataset were captured with distinct image sensors in different environments and recorded on different platforms. The LTIR dataset consists of 20 sequences, in which we considered four different challenging sequences to test the proposed method. Our method was also tested on another set of infrared images in the Photovoltaic System Thermal Image Database. The purpose of selecting thermal imagery in the Photovoltaic System Thermal Image Database is to identify the hotspots caused by various faults in the photovoltaic modules. The infrared images in the Photovoltaic System Thermal Image Database are captured at different altitudes under different weather conditions. The experiments were conducted on all the images of the databases listed in Table 2. A sample image from each sequence was considered to exhibit the thresholding results of our method along with other state-of-the-art methods as shown in Figure 5. To evaluate the performance of the proposed methods, the results of our method were compared with the ground truth images shown in Figure 5b and other image thresholding methods. Figure 5c shows the thresholding results of the Yen method, Figure 5d shows the thresholding results of the [PERSON] et al. method, Figure 5e shows the thresholding results of the [PERSON] et al. method, Figure 5f shows the thresholding results of the [PERSON] et al. method and Figure 5g shows the thresholding results of the proposed method. Although most of the results of the various methods are very close to the ground truth images, we can observe severe over-segmentation or under-segmentation in some of the results. We considered such severely over-segmented or under-segmented results to be poor performance results. The Yen method has produced poor results for Figure 5 iv,v; this reveals the inconsistency of this method in the thresholding of infrared images captured under different conditions. [PERSON] et al. has produced poor results for Figure 5 ii,iv,v, and [PERSON] et al. has produced poor results for Figure 5 iii,iv,v. Although the two methods were developed for infrared image segmentation, the results of these methods show incompetence in thresholding infrared images, especially in the case of multi-object infrared images. Although the [PERSON] et al. method produced satisfactory thresholding results for the first three images, this method produced over-segmented results for the next two images. It can be observed from the thresholding results that the proposed method yields consistent and satisfactory segmentation for all the images. Even though the proposed method produced better results compared to other methods, our method lags in providing more accurate results for high-resolution infrared thermal images, which we can observe in Figure 5 iv,f. The proposed method is successful in thresholding single person as well as multi-person infrared thermal images, which shows the significance of our method for the purpose of person detection applications.