UPC at CVPRW ActivityNet Challenge 2016
| Resource Type | Date |
|---|---|
| Software | 2016-06-18 |
Description
Main Contributor: Alberto Montes (BSc thesis at UPC ETSETB TelecomBCN Spring 2016).
Secondary Contributors: Santiago Pascual de la Puente, Amaia Salvador, Ignasi Esquerra and Xavier Giró-i-Nieto.
This software contains our proposed solution for both the classification and detection tasks of the ActivityNet Challenge 2016. We propose a system consisting of two different stages. First, the videos are organized in 16-frame clips, for which we individually extract both audio and visual features. Visual features were extracted from a pretrained 3D convolutional network (C3D), while MFCC coefficients were extracted for audio. On top of these features, we train a recurrent neural network to predict the activity sequence of each video at the granularity of the 16-frames clip.
Our submission obtained a mAP=0.58741 in the classification task, and a mAP=0.22369 in the detection task, according to the ActivityNet 2016 leaderboard.
Find the software and details in our repo on GitHub.
People involved
| Amaia Salvador | PhD Candidate |
| Xavier Giró | Associate Professor |
Related Publications
, “Temporal Activity Detection in Untrimmed Videos with Recurrent Neural Networks”, in 1st NIPS Workshop on Large Scale Computer Vision Systems 2016, 2016. (5.66 MB) |
|
, “Temporal Activity Detection in Untrimmed Videos with Recurrent Neural Networks”. 2016. (27.84 MB) |