# Video Contents Understanding using Deep Neural Networks

arXiv: https://arxiv.org/abs/2004.13959 · HTML: https://mlsanity.com/paper/2004.13959/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Apr 2020.

## Built on by (most cited first, 5 of 5)

- [A Concise Review of Transfer Learning](https://mlsanity.com/paper/2104.02144/) (2021)
- [Improving Neural Networks for Time Series Forecasting using Data Augmentation and AutoML](https://mlsanity.com/paper/2103.01992/) (2021)
- [The Use of Video Captioning for Fostering Physical Activity](https://mlsanity.com/paper/2104.03207/) (2021)
- [Gaussian Function On Response Surface Estimation](https://mlsanity.com/paper/2101.00772/) (2021)
- [Occupancy Detection in Room Using Sensor Data](https://mlsanity.com/paper/2101.03616/) (2021)

## Abstract (arXiv)

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.