Evaluating Two-Stream CNN for Video Classification

04/08/2015
by   Hao Ye, et al.
0

Videos contain very rich semantic information. Traditional hand-crafted features are known to be inadequate in analyzing complex video semantics. Inspired by the huge success of the deep learning methods in analyzing image, audio and text data, significant efforts are recently being devoted to the design of deep nets for video analytics. Among the many practical needs, classifying videos (or video clips) based on their major semantic categories (e.g., "skiing") is useful in many applications. In this paper, we conduct an in-depth study to investigate important implementation options that may affect the performance of deep nets on video classification. Our evaluations are conducted on top of a recent two-stream convolutional neural network (CNN) pipeline, which uses both static frames and motion optical flows, and has demonstrated competitive performance against the state-of-the-art methods. In order to gain insights and to arrive at a practical guideline, many important options are studied, including network architectures, model fusion, learning parameters and the final prediction methods. Based on the evaluations, very competitive results are attained on two popular video classification benchmarks. We hope that the discussions and conclusions from this work can help researchers in related fields to quickly set up a good basis for further investigations along this very promising direction.

READ FULL TEXT
research
06/09/2014

Two-Stream Convolutional Networks for Action Recognition in Videos

We investigate architectures of discriminatively trained deep Convolutio...
research
04/07/2015

Modeling Spatial-Temporal Clues in a Hybrid Deep Learning Framework for Video Classification

Classifying videos according to content semantics is an important proble...
research
11/12/2022

Deep Unsupervised Key Frame Extraction for Efficient Video Classification

Video processing and analysis have become an urgent task since a huge am...
research
12/01/2017

Video retrieval based on deep convolutional neural network

Recently, with the enormous growth of online videos, fast video retrieva...
research
02/25/2015

Exploiting Feature and Class Relationships in Video Categorization with Regularized Deep Neural Networks

In this paper, we study the challenging problem of categorizing videos a...
research
04/28/2015

Compact CNN for Indexing Egocentric Videos

While egocentric video is becoming increasingly popular, browsing it is ...
research
07/03/2018

Deep Architectures and Ensembles for Semantic Video Classification

This work addresses the problem of accurate semantic labelling of short ...

Please sign up or login with your details

Forgot password? Click here to reset