Efficient Activity Detection in Untrimmed Video with Max-Subgraph Search
| Autor: | Chao-Yeh Chen, Kristen Grauman |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2016 |
| Předmět: |
FOS: Computer and information sciences
Computer Vision and Pattern Recognition (cs.CV) Vertex connectivity Computer Science - Computer Vision and Pattern Recognition 02 engineering and technology Machine learning computer.software_genre Artificial Intelligence Activity detection 0202 electrical engineering electronic engineering information engineering 0501 psychology and cognitive sciences 050107 human factors Mathematics business.industry Applied Mathematics 05 social sciences Detector Video sequence Pattern recognition Computational Theory and Mathematics Binary classification Categorization Graph (abstract data type) 020201 artificial intelligence & image processing Computer Vision and Pattern Recognition Artificial intelligence business computer Classifier (UML) Software |
| Popis: | We propose an efficient approach for activity detection in video that unifies activity categorization with space-time localization. The main idea is to pose activity detection as a maximum-weight connected subgraph problem. Offline, we learn a binary classifier for an activity category using positive video exemplars that are "trimmed" in time to the activity of interest. Then, given a novel \emph{untrimmed} video sequence, we decompose it into a 3D array of space-time nodes, which are weighted based on the extent to which their component features support the learned activity model. To perform detection, we then directly localize instances of the activity by solving for the maximum-weight connected subgraph in the test video's space-time graph. We show that this detection strategy permits an efficient branch-and-cut solution for the best-scoring---and possibly non-cubically shaped---portion of the video for a given activity classifier. The upshot is a fast method that can search a broader space of space-time region candidates than was previously practical, which we find often leads to more accurate detection. We demonstrate the proposed algorithm on four datasets, and we show its speed and accuracy advantages over multiple existing search strategies. |
| Databáze: | OpenAIRE |
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