Abstract
Object recognition in the video sequence or images is one of the subfield of computer vision. Moving object recognition from a video sequence is an appealing topic with applications in various areas such as airport safety, intrusion surveillance, video monitoring, intelligent highway, etc. Moving object recognition is the most challenging task in intelligent video surveillance system. In this regard, many techniques have been proposed based on different methods. Despite of its importance, moving object recognition in complex environments is still far from being completely solved for low resolution videos, foggy videos, and also dim video sequences. All in all, these make it necessary to develop exceedingly robust techniques. This paper introduces multiple moving object recognition in the video sequence based on LoG Gabor-PCA approach and Angle based distance Similarity measures techniques used to recognize the object as a human, vehicle etc. Number of experiments are conducted for indoor and outdoor video sequences of standard datasets and also our own collection of video sequences comprising of partial night vision video sequences. Experimental results show that our proposed approach achieves an excellent recognition rate. Results obtained are satisfactory and competent.
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References
Hsu, Wallace: An industrial network flow information integration model for supply chain management and intelligent transportation. Enterprise Information Systems 1(3), 327–351 (2007)
Lipton, A.J., Fujiyoshi, H., Patil, R.S.: Moving target classification and tracking from real-time video. In: Proceedings of the IEEE Applications of Computer Vision, WACV 1998, pp. 8–14 (1998)
Petrovic, V.S., Cootes, T.F.: Vehicle type recognition with match refinement. In: International Conference on Pattern Recognition, vol. 3(8), pp. 95–98 (2004)
Lin, Y., Bhanu, B.: Evolutionary feature synthesis for object recognition. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews 35(2), 156–171 (2005)
Sullivan, G.D., Baker, K.D., Worrall, A.D., Attwood, C.I., Remagnino, P.M.: Model-based vehicle detection and classification using orthographic approximations. Image and Vision Computing 15(8), 649–654 (1997)
Bergboer, N.H., Postma, E.O., van den Herik, H.J.: Context-based object detection in still images. Image and Vision Computing 24(9), 987–1000 (2006)
Takano, S., Minamoto, T., Niijima, K.: Moving object recognition using wavelets and learning of eigenspaces, p.151 (1998)
Zanin, M., Messelodi, S., Modena, C.M.: An efficient vehicle queue detection system based on image processing. In: IEEE Proceedings Image Analysis and Processing, pp. 232–237 (2003)
Liu, C., Wechsler, H.: Independent Component Analysis of Gabor Features for Face Recognition. IEEE Trans. Neural Networks 14(4), 919–928 (2003)
Cook, J., Chandran, V., Sridharan, S., Fookes, C.: Gabor Filter Bank Representation for 3D Face Recognition. In: Proc. IEEE Digital Imaging Computing: Techniques and Applications, pp. 16–23 (2005)
Fields, D.: Relations between the statistics of natural images and the response properties of cortical cells. Journal of Optical Society of America 4(12), 2379–2394 (1987)
krishna, M.T.G., Ravishankar, M., Rameshbabu, D.R.: Ten-loPP: Tensor locality preserving projections approach for moving object detection and tracking. In: Meesad, P., Unger, H., Boonkrong, S. (eds.) IC2IT2013. AISC, vol. 209, pp. 291–300. Springer, Heidelberg (2013)
Lajevardi, S.M., Hussain, Z.M.: Facial Expression Recognition Using Log-Gabor Filters and Local Binary Pattern Operators. In: Proceedings of the International Conference on Communication, Computer and Power, pp. 349–353 (2009)
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Gopalakrishna, M.T., Ravishankar, M., Rameshbabu, D.R. (2014). Multiple Moving Object Recognitions in Video Based on Log Gabor-PCA Approach. In: Thampi, S., Abraham, A., Pal, S., Rodriguez, J. (eds) Recent Advances in Intelligent Informatics. Advances in Intelligent Systems and Computing, vol 235. Springer, Cham. https://doi.org/10.1007/978-3-319-01778-5_10
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DOI: https://doi.org/10.1007/978-3-319-01778-5_10
Publisher Name: Springer, Cham
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