Abstract
In this paper, we address the problem of recognizing unfinished human activity from partially observed videos. Specifically, we propose a novel human activity descriptor, which can represent pairwise relationships among human activities in a compact manner using pre-trained Convolutional Neural Networks (CNNs) by capturing the discriminative sub-volume. The potentially important relationship among all pairwise sub-volumes, called key-volumes, is automatically captured using global and local motion activation and the ratio of the participant. The captured key-volumes without prior knowledge hold discriminative information related to the unfinished activity. The key-volume information is considered in the descriptor construction procedure. Training a CNN model for a particular purpose requires a lot of resources, such as large amount of labeled data and computing power, despite its representational power. Thus, we develop a method to utilize pre-trained CNN without any additional model training procedure. The low-level features can be extracted through existing CNN toolkits. For a real application, the proposed method may be more cost-effective while implementing a smart surveillance system to understand human activity. In our experiments, we compare the performances of the proposed method with other state-of-the-art human activity prediction methods for two public datasets; the results of the experiments show that the proposed method outperforms these competing methods.
Original language | English |
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Title of host publication | 2016 23rd International Conference on Pattern Recognition, ICPR 2016 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 2060-2065 |
Number of pages | 6 |
ISBN (Electronic) | 9781509048472 |
DOIs | |
Publication status | Published - 2017 Apr 13 |
Event | 23rd International Conference on Pattern Recognition, ICPR 2016 - Cancun, Mexico Duration: 2016 Dec 4 → 2016 Dec 8 |
Other
Other | 23rd International Conference on Pattern Recognition, ICPR 2016 |
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Country/Territory | Mexico |
City | Cancun |
Period | 16/12/4 → 16/12/8 |
ASJC Scopus subject areas
- Computer Vision and Pattern Recognition