  We propose a novel application of Transfer Learning to classify video-frame
sequences over multiple classes. This is a pre-weighted model that does not
require to train a fresh CNN. This representation is achieved with the advent
of "deep neural network" (DNN), which is being studied these days by many
researchers. We utilize the classical approaches for video classification task
using object detection techniques for comparison, such as "Google Video
Intelligence API" and this study will run experiments as to how those
architectures would perform in foggy or rainy weather conditions. Experimental
evaluation on video collections shows that the new proposed classifier achieves
superior performance over existing solutions.
