Abstract
Research has shown that the application of an attention algorithm to the front-end of an object recognition system can provide a boost in performance over extracting regions from an image in an unguided manner. However, when video imagery is taken from a moving platform, attention algorithms such as saliency can lose their potency. In this paper, we show that this loss is due to the motion channels in the saliency algorithm not being able to distinguish object motion from motion caused by platform movement in the videos, and that an object recognition system for such videos can be improved through the application of image stabilization and saliency. We apply this algorithm to airborne video samples from the DARPA VIVID dataset and demonstrate that the combination of stabilization and saliency significantly improves object recognition system performance for both stationary and moving objects.
Keywords
- Support Vector Machine Classifier
- Scale Invariant Feature Transform
- Saliency Detection
- Scale Invariant Feature Transform Feature
- Video Stabilization
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
This work was partially supported by the Defense Advanced Research Projects Agency NeoVision2 program (contract No. HR0011-10-C-0033). The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressly or implied, of the Defense Advanced Research Projects Agency or the U.S. Government.
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Chen, Y., Khosla, D., Huber, D., Kim, K., Cheng, S.Y. (2011). A Neuromorphic Approach to Object Detection and Recognition in Airborne Videos with Stabilization. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2011. Lecture Notes in Computer Science, vol 6939. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24031-7_13
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DOI: https://doi.org/10.1007/978-3-642-24031-7_13
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