This work is related to building a Human Detection system based on You Only Look Once (YOLO) v4. It is one of the most recent Deep Learning approaches primitively built using single shot detection proposal. Unlike the double stage region-based object detection schemes this technique do not follow semantic segmentation, it does not undergo loss of the object information such as disappearance of the gradients and it does not require pre-defined anchors. This technique comprises strong feature extractors and reinforce multi scale object detection and it is very quick in the multi-threaded GPU environments. The YOLO v4 uses ground truth bounding boxes to extract the features like Weighted Residual Connections (WRC), Cross Stage Partial Connections (CSP), Cross mini Batch Normalization (CmBN), Self-Adversarial Training (SAT), Mish Activation (MA), Mosaic Data Augmentation (MDA) and Drop Block Regularization (DBR). Finally, the performance analysis of these model in terms of mean Average Precision (mAP) indicates that the modelling using YOLO v4 performs in a promising way and it can be used in automatic human and object detection systems.