# Anthropometric Ratios for Lower-Body Detection Based on Deep Learning and Traditional Methods
## 5 Discussion  
In this section, lower-body detection with the proposed anthropometric ratios is discussed from three perspectives: accuracy, complexity, and different image conditions. According to the results in Table 5, the proposed anthropometric ratios can be used to scale other detected parts of the human body to obtain lower-body ROIs. In addition, the A-CNNs, OpenPose and HOG-SVM methods achieve success in lower-body detection with high sensitivities of more than 80% because they can successfully detect and transform human shapes under various lighting and occlusion conditions. Regarding specificity, the \(VJ_{Face}\) algorithm provides higher specificity than the other methods for detection on the non-human data set because most background images consist of scenes such as sightseeing locations and mountains; therefore, the Haar-like rectangular templates rarely match these backgrounds. Regarding the performance of A-CNNs, it is sometimes not fair to use such background data sets in deep learning unless the background images are further categorized into subclasses, such as trees, appliances and buildings. To enhance the detection performance, a similar problem has been solved by using a one-class classifier based on a CNNs [57]. OpenPose was trained on the COCO data set [58], which contains images of two hundred fifty thousand people with keypoints [45]. Consequently, it seemed to provide the highest detection accuracy on the INRIA data set in this experiment. However, our A-CNNs model, which was trained on human body ROIs, could achieve higher specificity than OpenPose, which was trained on keypoints. According to Figure 22, OpenPose seems to be more suitable for human detection in a plain room than for application in an outdoor environment for exercise monitoring. For the purpose of lower-body detection, lower-body ROIs are based on the proposed anthropometric ratios, while OpenPose focuses on body keypoints, which need more optimization than lower-body ROIs based on the NASA Anthropometry and Biomechanics data [21]. Moreover, the proposed anthropometric ratios can also be modified for use in locating different parts of the body, such as the thigh, leg, and foot, to monitor lower-body activities without any need to retrain the A-CNNs model. In contrast, OpenPose would need to be retrained on a data set containing new keypoints for the detection of different body parts. According to the results in Table 6, the \(VJ_{Face}\) and \(VJ_{Upper}\) algorithms have relatively low complexity because they can reduce the complexity of the general algorithm from \(O(N,W)\) to \(O(P+4N_{r})\) based on the integral image calculation. Then, the complexity of the cascaded classifiers (with M filters and T thresholds) is \(O(MT)\). Therefore, the final complexity of the VJ algorithm is \(O((P+4N_{r})MT)\). In the case of HOG-SVM, the complexity of the HOG feature calculation is \(O(N_{r}W)\), which is multiplied by \(O(N_{f})\) (the number of features). The \(O(N_{f})\) complexity of the HOG calculation is high because of the iterations over \(N_{cell}\), \(N_{block}\) and \(N_{bin}\). If the dimensionality of the HOG features is not minimized to reduce the scale of \(O(N_{f})\), this algorithm might be too complex to be suitable for on-line detection. Subsequently, the complexity of the linear SVM is \(O(N_{f})\). Hence, the total complexity of HOG-SVM is \(O(N_{f}WN_{f}^{2})\). The A-CNNs method has a high classifier complexity, which depends on the number of convolutions. It also requires customizing a model to achieve high accuracy. The OpenPose method uses a CNNs model in its three main procedures for human detection. Moreover, the number of stages of each procedure influences the complexity of OpenPose. The number of stages should be optimized to achieve a suitable trade-off between accuracy and complexity [59].