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.
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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References
Serre, T., Poggio, T.: A Neuromorphic Approach to Computer Vision. Communications of the ACM (online) 53(10), 54–61 (2010)
Serre, T., Wolf, L., Bileschi, S., Riesenhuber, M., Poggio, T.: Robust Object Recognition with Cortex-Like Mechanisms. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI) 29(3), 411–426 (2007)
Itti, L., Koch, C.: A Saliency-Based Search Mechanism for Overt and Covert Shifts of Visual Attention. Vision Research 40, 1489–1506 (2000)
Guo, C., Ma, Q., Zhang, L.: Spatio-temporal Saliency Detection Using Phase Spectrum of Quaternion Fourier Tranform. In: Proc. CVPR, pp. 1–8 (2008)
Hou, X., Zhang, L.: Saliency Detection: A Spectral Residual Approach. In: Proc. CVPR, pp. 1–8 (2007)
Orabona, F., Metta, G., Sandini, G.: A Proto-object Based Visual Attention Model. In: Paletta, L., Rome, E. (eds.) WAPCV 2007. LNCS (LNAI), vol. 4840, pp. 198–215. Springer, Heidelberg (2007)
Huber, D., Khosla, D.: A Bio-Inspired Method and System for Visual Object-Based Attention and Segmentation. In: Proc. SPIE DSS, vol. 7696 (2010)
Chikkerur, S., Serre, T., Poggio, T.: Attentive Processing Improves Object Recognition. Massachusetts Institute of Technology Technical Report: MIT-CSAIL-TR-2009-046 (2009)
Rutishauser, U., Walther, D., Koch, C., Perona, P.: Is Bottom-Up Attention Useful for Object Recognition? In: Proc. CVPR, vol. 2, pp. 37–44 (2004)
Walther, D., Koch, C.: Modeling Attention to Salient Proto-Objects. Neural Networks 19, 1395–1407 (2006)
Mutch, J., Lowe, D.: Multiclass Object Recognition with Sparse, Localized Features. In: Proc. CVPR, pp. 11–18 (2006)
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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
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-24030-0
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