DMCA
Learning latent temporal structure for complex event detection (2012)
| Venue: | In CVPR |
| Citations: | 75 - 2 self |
BibTeX
@INPROCEEDINGS{Tang12learninglatent,
author = {Kevin Tang and Li Fei-fei and Daphne Koller},
title = {Learning latent temporal structure for complex event detection},
booktitle = {In CVPR},
year = {2012}
}
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Abstract
In this paper, we tackle the problem of understanding the temporal structure of complex events in highly varying videos obtained from the Internet. Towards this goal, we utilize a conditional model trained in a max-margin framework that is able to automatically discover discriminative and interesting segments of video, while simultaneously achieving competitive accuracies on difficult detection and recognition tasks. We introduce latent variables over the frames of a video, and allow our algorithm to discover and assign sequences of states that are most discriminative for the event. Our model is based on the variable-duration hidden Markov model, and models durations of states in addition to the transitions between states. The simplicity of our model allows us to perform fast, exact inference using dynamic programming, which is extremely important when we set our sights on being able to process a very large number of videos quickly and efficiently. We show promising results on the Olympic Sports dataset [16] and the 2011 TRECVID Multimedia Event Detection task [18]. We also illustrate and visualize the semantic understanding capabilities of our model. 1.
Keyphrases
latent temporal structure complex event detection competitive accuracy interesting segment dynamic programming max-margin framework complex event temporal structure recognition task latent variable conditional model trecvid multimedia event detection task large number semantic understanding capability exact inference difficult detection olympic sport dataset variable-duration hidden markov model promising result model duration
