A Fisher Kernel Approach for Multiple Instance Based Object Retrieval in Video Surveillance
This paper presents an automated surveillance system that exploits the Fisher Kernel representation in the context of multiple-instance object retrieval task. The proposed algorithm has the main purpose of tracking a list of persons in several video sources, using only few training examples. In th...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Stefan cel Mare University of Suceava
2015-11-01
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| Series: | Advances in Electrical and Computer Engineering |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.4316/AECE.2015.04006 |
| Summary: | This paper presents an automated surveillance system that exploits the Fisher Kernel representation
in the context of multiple-instance object retrieval task. The proposed algorithm has the main purpose
of tracking a list of persons in several video sources, using only few training examples. In the
first step, the Fisher Kernel representation describes a set of features as the derivative with
respect to the log-likelihood of the generative probability distribution that models the feature
distribution. Then, we learn the generative probability distribution over all features extracted
from a reduced set of relevant frames. The proposed approach shows significant improvements and
we demonstrate that Fisher kernels are well suited for this task. We demonstrate the generality
of our approach in terms of features by conducting an extensive evaluation with a broad range of
keypoints features. Also, we evaluate our method on two standard video surveillance datasets
attaining superior results comparing to state-of-the-art object recognition algorithms. |
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| ISSN: | 1582-7445 1844-7600 |