Due to the fact of increase in amount of video data each day, the need for auto generation of captioning them clearly is inevitable. Video captioning makes the video more accessible in numerous ways. It allows the deaf and hard of hearing individuals to watch videos, helps people to focus on and remember the information more easily, and lets people watch it in sound- sensitive environments. Video captioning refers to the task of generating a natural language sentence that explains the content of the input video clips. The events are temporally localized in the video with independent start and end times. At the same time, some events that might also occur concurrently and overlap in time. Classifying the events into present, past and future as well as separating them based on their start and end times will help in identifying the order of events. Hence the proposed work develops a captioning system that clearly explains each visual feature that is present in the image conceptually. The Blended-LSTM (Bl- LSTM) model with the help of Xception based Convolution Neural Network (CNN) with Fusion Visual Captioning (FVC) system achieves it with the BLEU score of 75.9%.