## 2 Related Works
However, the system has limitations in requiring high-quality resolution of image data and multiple cameras to cope with self-occlusions. In addition, a method of estimating real-time, multi-person, two-dimensional poses is proposed [19] by using part affinity fields of skeleton images. This architecture is designed to jointly study human-body part locations to recognize human poses by utilizing a convolutional neural network (_CNN_) trained with a large amount of data. However, the results which are tested on different datasets show that the detection of body parts could not adequately differentiate human subjects from objects similar to the human body. Therefore, a suggestion for improving this system involves embedding an improved background subtraction technique during the pre-processing stage. This kind of extended body part model might provide the research for the detection of abnormal or falls and normal daily activities. Moreover, the features based on the body shape of the silhouette images are investigated for patterns of human movement in the literature survey [20]. The boundary points of the body are extracted and the distances are calculated from the centroid of the object. Moreover, several physical features could be observed for the gait period, stride length, and height, as well as the ratio of chest width to body height with respect to analyzing the patterns of walking slow, fast and normal. However, most of the research that focused on an analysis of walking patterns mentions that each individual person has a unique walking style, and it is necessary to pre-select according to age, and gender, as well as whether subjects are healthy or not. In analyzing abnormal and normal events, most systems utilize the coefficient of motion and prefixed threshold features [5; 7; 16; 21]. The proposed method [22] uses four features as the inputs to a k-nearest neighbor (_K-NN_) classifier to detect a fall: orientation angle, ratio of fitted ellipse, coefficient of motion, and silhouette threshold. The overall accuracy of the system is 95% in real time video sequences. Similarly, a fall-detection method is proposed [23] that depends on the activity patterns of the detected person, such as the aspect ratio and speed of motion of the human object. The region based convolutional neural network (_R-CNN_) deep learning algorithm is then selected to obtain information on the object's position in the video sequences during a fall. This method can be used to classify falls during normal daily activities with an accuracy of 95.50% using the simulated video sequences. Moreover, [PERSON] 2013 [24] observed fourteen features extracted from the detected bounding box, which includes the aspect ratio, centroid, and ellipse orientation for the detection of falls. The combination of Fourier and wavelet transforms, using first and second derivatives, is also utilized in determining these features. An evaluation is then performed using the support vector machine (_SVM_) and adaptive boosting (AdaBoost) classifiers. The system achieves an accuracy of 99.42%, a precision of 95.91%, and a recall of 92.15% using the Le2i fall detection dataset. In addition, another method for the detection of unnatural falls is proposed in 2018 [25] by extracting the features of aspect ratio, orientation angle, motion history image, and objects below the threshold line. The obtained features are utilized as the inputs for the detection system by applying _SVM_, _K-NN_, Stochastic Gradient Descent (_SGD_), Decision Tree (_DT_) and Gradient Boosting (_GB_). The observed performance of the detection system using Decision Tree (_DT_) provides an accuracy of 95%, a precision of 94%, and a recall of 95% in the Le2i fall detection dataset. Then, The proposed fall-detection system [5], in support of independent living for older adults, generates features for classifying falls by extracting motion information using a best-fit ellipse and a bounding box around the silhouetted object, projection histograms, and estimates for head position over time.