Research on the BOVW Model of Infrared Video Based on the Motion History

Abstract:

Aiming at the feature representation problem of infrared video, this paper presents an approach for bag of visual words model by considering the spatial and the temporal relations among the visual features. In this approach, motion mask image was generated based on motion history image, optical flow vector is mapping to visual code book. By using motion history value and motion energy value calculating the weighting coefficient, the BOVW was constructed based on angle voting. Experiments have demonstrated improved performances on motion feature representation in the infrared video.

• Periodical: Applied Mechanics and Materials (Volumes 602-605)
• Pages: 3652-3660
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.602-605.3652
• Online since: August 2014
• Authors: Lin Tang*, Lin Liu, Yu Wei
• Keywords: Bag of Visual Words, Infrared Imaging, Motion History

References

[1] J. Willamowski, D. Arregui, G. Csurka, C. R. Dance, and L. Fan. Categorization nine visual classes using local appearance descriptors. In IWLAVS, (2004).

[2] D. Nister and H. Stewenius. Scalable recognition with a vocabulary tree. In CVPR06, pages 2161–2168, (2006).

[3] Rong Zhao, William I. Grosky. Narrowing the Semantic Gate-Improved Text Based Web Document Retrieval Using Visual Features[J]. IEEE Transactions on multimedia. 2002, 4(2): 189 – 200.

DOI: 10.1109/tmm.2002.1017733

[4] C. D. Manning, P. Raghavan, H. Schutze. Introduction to information retrieval [J]. Cambridge University Press. (2008).

[5] P. Doll´ar, V. Rabaud, G. Cottrell, and S. Belongie. Behavior recognition via sparse spatio-temporal features. In VSPETS, October (2005).

DOI: 10.1109/vspets.2005.1570899

[6] C. Schuldt, I. Laptev, and B. Caputo. Recognizing human actions: a local svm approach. In ICPR04, pages III: 32–36, (2004).

DOI: 10.1109/icpr.2004.1334462

[7] S. Savarese, J. Winn, and A. Criminisi. Discriminative object class models of appearance and shape by correlatons. In CVPR06, pages 2033–2040, (2006).

DOI: 10.1109/cvpr.2006.102

[8] M. Marszaek and C. Schmid. Spatial weighting for bag-offeatures. In CVPR06, pages 2118–2125, (2006).

[9] K. Grauman and T. Darrell. The pyramid match kernel: Discriminative classification with sets of image features. InICCV05, pages 1458–1465, (2005).

DOI: 10.1109/iccv.2005.239

[10] S. Lazebnik, C. Schmid, and J. Ponce. Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories. In CVPR06, pages 2169–2178, (2006).

DOI: 10.1109/cvpr.2006.68

[11] Sebastiani, F. Machine learning in automated text categorization [J]. Acm Computing Surveys, Mar, 2002, 34 (1): 1-47.

DOI: 10.1145/505282.505283

[12] X. Wang, K. Tieu, and E. Grimson. Learning semantic scene models by trajectory analysis. In Proc. ECCV, (2006).

[13] H. Zhong, J. Shi, and M. Visontai. Detecting unusual activity in video. In Proc. CVPR, (2004).

[14] P. Smith, N. V. Lobo, and M. Shah. Temporalboost for event recognition. In Proc. ICCV, (2005).

[15] Ahad M A R, Tan J K, Kim H, et al. Motion history image: its variants and applications[J]. Machine Vision and Applications, 2012, 23(2): 255-281.

DOI: 10.1007/s00138-010-0298-4

[16] Yin Z, Collins R. Moving object localization in thermal imagery by forward-backward MHI[C]/Computer Vision and Pattern Recognition Workshop, 2006. CVPRW'06. Conference on. IEEE, 2006: 133-133.

DOI: 10.1109/cvprw.2006.131

[17] Barron, Beauchemin, Fleet．On Optical Flow[J]. AIICSR, 1994. 9.

[18] ] Li, J., Allinson, N. M. A comprehensive review of current local features for computer vision [J]. Neurocomputing, Jun, 2008, 71 (10-12): 1771-1787.

DOI: 10.1016/j.neucom.2007.11.032