###### Abstract
Once the training framework is ready, removing alike features from the test image samples and, as a consequence, predicting the proper class from features using the trained framework for each provided test image are feasible. Several techniques were proposed in light of the efficient extraction of features as well as classification to detect arbitrary objects in images [4]. Over the past two decades, the focus had been on the design of efficient hand-crafted features to improve detection robustness and accuracy. A diverse set of extraction techniques was provided by the vision research community such as Scale Invariant Feature Transform (SIFT), [PERSON]  
Figure 1: Example of machine learning work flow for object classification across three different classes (bird, human, and lion). (VJ), Histogram of Oriented Gradients (HoG), Speeded Up Robust Features (SURF), and Deformable Part-Based Models (DPM) [5,6]. Deep learning techniques have effectively combined the task of extracting the features and classification in an end-to-end way [4]. Convolutional neural networks (CNNs) have become quite popular for tackling various problems, among which includes object detection. Subsequently, the performance of such architectures has led to a proliferation in both achievable speed and accuracy. The object detection methods using deep CNN such as Spatial Pyramid Pooling Networks (SPPNets), Region-based CNN (R-CNN), Feature Pyramid Networks (FPNs), fast RCNN, You Only Look Once (YOLO), faster R-CNN, Single-Shot Multibox Detector (SSD), and Region-based Fully Convolutional Networks (R-FCNs) have shown excellent benefits relative to state-of-the-art ML methods [7,8]. This article focuses on a specific sub-domain of detection, which is the human detection. In a year, over a billion people lost their lives and around 20-50 million people experienced fatal complications as a result of traffic accidents [9]. In 2015, more than 5000 pedestrians died in traffic accidents, while about 130,000 pedestrians required medical care for non-fatal problems in the United States. However, the ratio of traffic fatality can be reduced or even eliminated by utilizing various detection techniques in autonomous vehicles that use sensors to interact with other neighboring vehicles in the vicinity [10]. With increases in crime and public fear of terrorism, public security has become an unavoidable concern, and human detection techniques can be employed to monitor and control public spaces remotely. Approximately 21,000 people lose their lives because of terrorist activities every year and 0.05% of the total deaths in 2017 occurred due to terrorism [11]. The necessity to install a sufficient number of human-detecting devices has spiked in public locations following tragedies in London, New York, and other cities across the globe. Such incidents are critical enough and demand a robust design and global deployment of such systems. Hence, human-detection systems are observed as a viable answer for ensuring public safety and have become one of the most significant study fields today. The detection of human beings is one of the key responsibilities in the field of computer vision. It is indeed difficult to identify human in pictures because of several background effects such as occlusions [12], illuminated conditions and background clutters [13]. Previous techniques have been unsuccessful in real-world scenarios for detecting humans, as they took a longer period of time for detection and yielded outcomes that were not sufficiently accurate due to distance as well as changes in appearance [6]. Therefore, a universal representation of objects still continues to remain an open challenge in midst of such factors. Human detection is currently being utilized for many applications.