Video classification task has gained a significant success in the recent
years. Specifically, the topic has gained more attention after the
emergence of deep learning models as a successful tool for automatically
classifying videos. In recognition to the importance of video
classification task and to summarize the success of deep learning models
for this task, this paper presents a very comprehensive and concise
review on the topic. There are a number of existing reviews and survey
papers related to video classification in the scientific literature.
However, the existing review papers are either outdated, and therefore,
do not include the recent state-of-art works or they have some
limitations. In order to provide an updated and concise review, this
paper highlights the key findings based on the existing deep learning
models. The key findings are also discussed in a way to provide future
research directions. This review mainly focuses on the type of network
architecture used, the evaluation criteria to measure the success, and
the data sets used. To make the review self- contained, the emergence of
deep learning methods towards automatic video classification and the
state-of-art deep learning methods are well explained and summarized.
Moreover, a clear insight of the newly developed deep learning
architectures and the traditional approaches is provided, and the
critical challenges based on the benchmarks are highlighted for
evaluating the technical progress of these methods. The paper also
summarizes the benchmark datasets and the performance evaluation
matrices for video classification. Based on the compact, complete, and
concise review, the paper proposes new research directions to solve the
challenging video classification problem.