BIOLOGICALLY INSPIRED ATTENTIVE MOTION ANALYSIS FOR VIDEO SURVEILLANCE
Florian Raudies, Heiko Neumann
2008
Abstract
Recently proposed algorithms in the field of vision-based video surveillance are build upon directionally consistent flow (Wixson and Hansen, 1999; Tian and Hampapur, 2005), or statistics of foreground and background (Ren et al., 2003; Zhang et al., 2007). Here, we present a novel approach which utilizes an attention mechanism to focus processing on (highly) suspicious image regions. The attention signal is generated through temporal integration of localized image features from monocular image sequences. This approach incorporates biologically inspired mechanisms, for feature extraction and spatio-temporal grouping. We compare our approach with an existing method for the task of video surveillance (Tian and Hampapur, 2005) with a receiver operator characteristic (ROC) analysis. In conclusion our model is shown to yield results which are comparable with existing approaches.
References
- Barron, J., Fleet, D., and Beauchemin, S. (1994). Performance of optical flow techniques. IJCV, pages 43-77.
- Brown, L., Senior, A., Tian, Y.-L., Connell, J., Hampapur, A., Shu, C.-F., Merkl, H., and Lu, M. (2005). Performance evaluation of surveillance systems under varying conditions. IEEE Int'l Workshop on Performance Evaluation of Tracking and Surveillance.
- Daugman, J. (1988). Complete discrete 2-D Gabor transforms by neural networks for image analysis and compression. Trans. Acoustics, Speech, and Signal Proc., 26(7):1169-1179.
- Deneve, S., Latham, P., and Pouget, A. (1999). Reading population codes: a neural implementation of ideal observers. Nature Neuroscience, 2:740-745.
- F örstner, W. (1986). A feature based correspondence algorithm for image matching. ISP Comm. III, Rovaniemi 1986, International Archives of Photogrammetry, pages 26-3/3.
- Hubel, D. and Wiesel, T. (1968). Receptive fields and functional architecture of monkey striate cortex. J. Physiol., (195):215-243.
- Lucas, B. and Kanade, T. (1981). An iterative image registration technique with an application to stereo vision. Proc. DARPA Image Understanding Workshop, pages 121-130.
- Majchrzak, D., Sarkar, S., Sheppard, B., and Murphy, R. (2000). Motion detection from temporally integrated images. In Proc. IEEE 15th ICPR, pages 836-839.
- Marr, D. and Ullman, S. (1981). Direction selectivity and its use in early visual processing. Proc. Royal Soc. of London, B, 211:151-180.
- Neumann, H. and Sepp, W. (1999). Recurrent V1-V2 interaction in early visual boundary processing. Biol. Cybernetics, 81:425-444.
- Ren, Y., Chua, C.-S., and Ho, Y.-K. (2003). Motion detection with nonstationary background. Machine Vision and Applications, 13:332-343.
- Rothenstein, A. and Tsotsos, J. (2007). Attention links sensing to recognition. Image and Vision Computing. (in press).
- Tian, Y.-L. and Hampapur, A. (2005). Robust salient motion detection with complex background for real-time video surveillance. Proc. IEEE Workshop on Motion and Video Computing, pages 30-35.
- Tsotsos, J., Liu, Y., Martinze-Trujiloo, J., Pomplun, M., Simine, E., and Zhou, K. (2005). Attending to visual motion. Computer Vision and Image Understanding, 100:3-40.
- Weidenbacher, U., Bayerl, P., Neumann, H., and Flemming, R. (2006). Sketching shiny surfaces: 3D shape extraction and depicting of specular surfaces. ACM Trans. on Applied Perception, 3:262-285.
- Wixson, L. and Hansen, M. (1999). Detecting salient motion by accumulating directionally-consistent flow. In Proc. of the Seventh IEEE ICCV, pages 797-804.
- Zhang, W., Fang, X., Yang, X., and Wu, Q. (2007). Spatiotemporal gaussian mixture model to detect moving objects in dynamic scenes. J. of Electronic Imaging, 16.
- Zhou, Q. and Aggarwal, J. (2001). Tracking and classifying moving objects from video. In IEEE Int. Workshop on PETS.
Paper Citation
in Harvard Style
Raudies F. and Neumann H. (2008). BIOLOGICALLY INSPIRED ATTENTIVE MOTION ANALYSIS FOR VIDEO SURVEILLANCE . In Proceedings of the Third International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2008) ISBN 978-989-8111-21-0, pages 645-650. DOI: 10.5220/0001078806450650
in Bibtex Style
@conference{visapp08,
author={Florian Raudies and Heiko Neumann},
title={BIOLOGICALLY INSPIRED ATTENTIVE MOTION ANALYSIS FOR VIDEO SURVEILLANCE},
booktitle={Proceedings of the Third International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2008)},
year={2008},
pages={645-650},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001078806450650},
isbn={978-989-8111-21-0},
}
in EndNote Style
TY - CONF
JO - Proceedings of the Third International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2008)
TI - BIOLOGICALLY INSPIRED ATTENTIVE MOTION ANALYSIS FOR VIDEO SURVEILLANCE
SN - 978-989-8111-21-0
AU - Raudies F.
AU - Neumann H.
PY - 2008
SP - 645
EP - 650
DO - 10.5220/0001078806450650