# DeepSOCIAL: Social Distancing Monitoring and Infection Risk Assessment in COVID-19 Pandemic
## 4 Model Training and Experimental Results
### Performance Evaluation  
In order to test the performance of the proposed model, we used the Oxford Town Centre (OTC) dataset [31] as a previously unseen and challenging dataset with very frequent cases of occlusions, overlaps, and crowded zones. The dataset also contained a good diversity of human specimens in terms of clothes and appearance in a real-world public place. In order to provide a similar condition for performance analysis of YOLO based models, we fine-tuned each model on human categories of the Google Open Images (GOI) [107] data set. This was done by removing the last layer of each model and placing a new layer (with random values of the uniform probability distribution) corresponding to a binary classification (presence or absence of a human). Furthermore, in order to provide an equal condition for the speed and generalisability, we also tested each of the trained models against the OTC dataset [31]. We evaluated and compared our developed models against three common metrics of object detection in computer vision, including Precision Rate, Recall Rate, and FPS against three state-of-the-art human/object detection methods. All of the benchmarking tests and comparisons were conducted on the same hardware and software: a Windows 10-based platform with an Intel\({}^{\copyright}\) Core\({}^{\text{TM}}\) i5-3570K processor and an NVIDIA RTX 2080 GPU with CUDA version 10.1. In terms of mass deployment of the system, the above hardware setup can handle up to 10 input cameras for real-time monitoring of e.g., different floors and angles of large shopping malls. However, for smaller scales, a cheaper RTX 1080 GPU or an 8-core/16-thread 10 th generation Core\({}^{\text{TM}}\) i7 CPU would suffice for real-time performance. Figure 10 illustrates the development of loss function in training and validations phases for four versions of our DeepSOCIAL model with different backbone structures. The graphs confirm a fast yet smooth and stable transition for minimising the loss function in DS version after 1090 epochs where we reached to an optimal trade-off point for both the training and validation loss. Table 3 provides the details of each backbone and the outcome of the experimental results against three more state-of-the-art model on the OTC Dataset. Figure 10: Training and validation loss of the DeepSOCIAL models over the Open Images dataset. Figure 11 visualises the robustness of the proposed detectors in three challenging indoor/outdoor publicly available datasets: Oxford Town Centre, Mall Dataset, and Train Station Dataset. Interestingly, the Faster-RCNN model showed good generalisability; however, its low speed was an issue which seems to be due to the computational cost of the \"region proposal\" technique. Since the system required a real-time performance, any model with the speeds slower than 10 _fps_ and/or a low level of accuracy may not be a suitable option for Social Distancing monitoring. Therefore, SSD and Faster-RCNN failed in this benchmarking assessment, despite their popularity in other applications. YOLOv3 and YOLOv4 based DeepSOCIAL-\(X\) methods provided relatively better results comparing the other models, and finally, the proposed DeepSOCIAL-DS model outperformed all of the assessed models in terms of both speed and accuracy. Figure 12 provides sample footage of the challenging scenarios when the people either entered or exited the scene, and only part of their body (e.g., their feet) was visible. The figure clearly indicates the strength of DeepSOCIAL in Row (a), comparing to the state-of-the-art. The bottom row (d) with blue bounding boxes shows the ground-truth while some of the existing people with partial visibility are not annotated even in original ground-truth dataset. Row (c), YOLOv3 shows a couple of more detections; however, the IoU of the suggested bounding boxes are low and some of them can be counted as false positives. Row (b), the standard YOLOv4-based detector, shows a significant improvement comparing to row (c) and is considered as the second-best.