Object Oriented Security - Aerial surveillance plays an important role for security applications. It can be further used to monitor borders, restricted zones and critical infrastructure. With the help of drones one can perform surveillance and get the exact location of various objects. Aerial object detection comes with many challenges like the object size which can be as low as 20×20 pixels. Images taken from satellites are hundreds of megapixels. Traditional methods like Histogram of oriented gradients (HOG) and Scale invariant feature transformation (SIFT) were used to extract features from the objects. Then these features were given to machine learning classifier like logistic regression, Support vector machine (SVM) and Random forest (RF) for detection and classification. However, the issue with these methods is that they are highly inaccurate and generated many false detections and misclassifications too. With the evolution of Graphics processing units (GPU) and the introduction of convolutional neural networks (CNN) as well as Deep Learning algorithms situation got changed. Now, it is possible to extract more information and provide better accuracy. In this paper for object detection You only look once version 4 (YOLOv4) is used which is one of the state-of-the-art algorithms. It uses Darknet 53 which is a type of CNN as a backbone for feature extraction. In this work the YOLOv4 based proposed system detect and localize vehicles present in the restricted zone and then geotag them.