## 2 Related Literature: State-of-the-Art Methods  
Human detection is the process of identifying each object in a static image or image sequences that are regarded to be human. Human detection is widely acknowledged to have advanced through two different historical periods in recent decades: \"conventional human detection period (before 2012)\" and \"deep learning-based detection period (after 2012)\", as illustrated in Figure 2.  
Human detection is typically accomplished by extracting regions of interest (ROI) from an arbitrary image sample, illustrating the regions using descriptors, and then categorizing the regions as non-human or human, accompanied by post-processing processes [27]. In conventional techniques, human descriptors are generally designed by locally removing the features. A few examples include \"edge-based shape features (e.g., [28])\", \"appearance features (e.g., color [29], texture [30])\", \"motion features (e.g., temporal differences [31])\", \"optical flows [32]\", and their combinations [33]. Most of their functions are manually designed, which benefit from the ease of description and intuitively comprehending them. In addition, they were shown to perform well with limited collections of training datasets. The Deformable Part-based Model (DPM) is the earlier state-of-the-art approach for detection process [34]. DPM is considered an extension of the histograms of the oriented gradients (HOG) model. The projected object is scored using the entire image's coarse global template as well as the six higher-resolution portions of the object. HOG is used to characterize every single input. Following this approach, HOG's multi-model can address the varying viewpoint problem. In the training phase, a latent support vector machine (latent SVM) was employed to decrease the detection drawback relative to the classification area. The coordinates of the component are considered as the latent element. This approach resulted in a massive impact due to its robustness. Manually described features on the other hand, are unable to present more detailed information about the objects. In particular, they were challenged by the background, occlusion, motion blur, and illumination conditions. Hence, deep learning algorithms are regarded as relatively more efficient in human detection because they can learn more sophisticated features from images [35; 36; 37]. Although these initial deep algorithms have demonstrated some improvements over the classical models, these functions are still constructed manually, and the key concept is to expand the earlier models. Deep CNNs are also applied for the feature extraction in a few studies, for example [38]. A complexity perception cascade training for human detection was performed followed by the extraction of features. Deep learning approaches are currently being used to address many identification problems in several ways. One of the most promising architectures is the Convolutional Neural Network (CNN). Deep CNNs can learn object features on their own; thus, they depend less on the object's classes. Training a class-independent method, contrastingly, means that more data will be used for learning as deep learning requires a significant volume of data relative to training a domain-specific method. Only a few articles have been published in the field of human detection using the CNNs method. [PERSON] et al. [39] employed a CNN to learn human segmentation characteristics (e.g., hats and backpacks), but the network component leads to boosting the prediction accuracy by re-classifying the prediction item as negative or positive, rather than making predictions directly. [PERSON] et al. [40] included a sub-network relative to a novel network built on Fast R-CNN to deal with small-scale objects. [PERSON] et al. [41] straightforwardly examined a cross-class detection method (CNN), which involved faster R-CNNs performances on independent pedestrian detection, and came up with good findings. Among the three techniques, besides [39], which does not directly deal with detection, refs.