Anomaly detection in unstructured environments using Bayesian nonparametric scene modeling. Girdhar, Y., Walter Cho, Campbell, M., Pineda, J., Clarke, E., & Singh, H. In 2016 IEEE International Conference on Robotics and Automation (ICRA), pages 2651-2656, 5, 2016. IEEE. ![Anomaly detection in unstructured environments using Bayesian nonparametric scene modeling [link]](https://bibbase.org/img/filetypes/link.svg "link")
Website doi abstract bibtex 1 download This paper explores the use of a Bayesian non-parametric topic modeling technique for the purpose of anomaly detection in video data. We present results from two experiments. The first experiment shows that the proposed technique is automatically able characterize the underlying terrain, and detect anomalous flora in image data collected by an underwater robot. The second experiment shows that the same technique can be used on images from a static camera in a dynamic unstructured environment. The second dataset consisting of video data from a static seafloor camera, capturing images of a busy coral reef. The proposed technique was able to detect all three instances of an underwater vehicle passing in front of the camera, amongst many other observations of fishes, debris, lighting changes due to surface waves, and benthic flora.
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title = {Anomaly detection in unstructured environments using Bayesian nonparametric scene modeling},
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abstract = {This paper explores the use of a Bayesian non-parametric topic modeling technique for the purpose of anomaly detection in video data. We present results from two experiments. The first experiment shows that the proposed technique is automatically able characterize the underlying terrain, and detect anomalous flora in image data collected by an underwater robot. The second experiment shows that the same technique can be used on images from a static camera in a dynamic unstructured environment. The second dataset consisting of video data from a static seafloor camera, capturing images of a busy coral reef. The proposed technique was able to detect all three instances of an underwater vehicle passing in front of the camera, amongst many other observations of fishes, debris, lighting changes due to surface waves, and benthic flora.},
bibtype = {inproceedings},
author = {Girdhar, Yogesh and Walter Cho, undefined and Campbell, Matthew and Pineda, Jesus and Clarke, Elizabeth and Singh, Hanumant},
doi = {10.1109/ICRA.2016.7487424},
booktitle = {2016 IEEE International Conference on Robotics and Automation (ICRA)}
}
Downloads: 1
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