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Action Recognition with Artificial Neural Networks (Master Thesis 13/07/2015)

This repository contains the code developed to extract and process features from the dataset UCF-101 using DenseTrajectories.

Having extracted these features, a Neural Network at trajectory-patch level is trained in order to classify the activity that is being executed in the video (i.e: playing a piano, walking, ...). In order to do that, it has been proposed different voting schemes.


* Extraction performed under a SunGridEngine cluster.
* Using DenseTrajectories as feature extractor.
* Training and evaluation performed using [deepnet]( with [Eigen](

Frames of UCF-101 videos:

Frame examples from UCF-101 dataset

improved Dense Trajectories (iDT):

alt text

Features are extracted around de detected movement, like a 3D patch.

iDT example

Different features are extracted:

* Histograms of Oriented Gradients (HOG) 
* Histograms of Optical Flow (HOF) 
* Motion Boundary Histograms (MBH)
* Trajectory's information

All these values are composed in a 435 feature vector.

Classification model: Neural Network

We have used a Neural Network at patch level, learning how to classify each patch into one of the 101 classes. Once the training is completed, we can extract features from one video and then classify each patch individually.

Proposed architecture

Finally, we have used different voting schemes, i.e: posterior sum or most voted, weighting with all the patches in one video, in order to provide the final label.

Example of different voting schemes

This final procedure improves in every case the classification, obtaining an improvement of 40 percentual points over using just the label provided at patch level.


This repository contains the code developed to extract and process features from the dataset UCF-101 and then perform the associated classification task, that is, classifying the activity.




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