Moving object detection and segmentation in the field of computer vision is gaining much interest amongst researchers for its wide variety of application. Moving object detection aims at captivating the moving objects in a video sequence whereas moving object segmentation focuses on segmenting the objects in motion from a stationary background in a video sequence. This paper introduces both these computer vision tasks in detail along with its various applications and challenges. A comprehensive literature review in this field is provided in the next section. Further, the various deep learning approaches for moving object detection and segmentation are detailed with their advantages and disadvantages. The various available dataset for surveillance applications along with their properties are elaborated. Next section explains the numerous evaluation metrics that are used to evaluate the performance of the model. The qualitative results are detailed along with graphs and figures. At last, the study is concluded with future research directions in this field.