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Erschienen in:
Report on NSF/ARO/ONR Workshop on Distributed Camera Networks: Research Challenges and Future Directions Kapitel lesen Erstes Kapitel lesen

2011 | OriginalPaper | Buchkapitel

4. Multiple-View Object Recognition in Smart Camera Networks

verfasst von : Allen Y. Yang, Subhransu Maji, C. Mario Christoudias, Trevor Darrell, Jitendra Malik, S. Shankar Sastry

Erschienen in: Distributed Video Sensor Networks

Verlag: Springer London

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Abstract

We study object recognition in low-power, low-bandwidth smart camera networks. The ability to perform robust object recognition is crucial for applications such as visual surveillance to track and identify objects of interest, and overcome visual nuisances such as occlusion and pose variations between multiple camera views. To accommodate limited bandwidth between the cameras and the base-station computer, the method utilizes the available computational power on the smart sensors to locally extract SIFT-type image features to represent individual camera views. We show that between a network of cameras, high-dimensional SIFT histograms exhibit a joint sparse pattern corresponding to a set of shared features in 3-D. Such joint sparse patterns can be explicitly exploited to encode the distributed signal via random projections. At the network station, multiple decoding schemes are studied to simultaneously recover the multiple-view object features based on a distributed compressive sensing theory. The system has been implemented on the Berkeley CITRIC smart camera platform. The efficacy of the algorithm is validated through extensive simulation and experiment.

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Vorheriges Kapitel Projective Joint Invariants for Matching Curves in Camera Networks
Nächstes Kapitel A Comparison of Techniques for Camera Selection and Hand-Off in a Video Network
Fußnoten
1
The strategy of choosing varying sampling rate is a direct application of the celebrated Slepian–Wolf theorem [25]. In a nutshell, the theorem shows that, given two sources X 1 and X 2 that generate sequences x 1 and x 2, asymptotically, the sequences can be jointly recovered with vanishing error probability if and only if
$$R_1 > H(X_1 | X_2), \qquad R_2 > H(X_2 | X_1), \qquad R_1 + R_2 > H(X_1, X_2),$$
where R is the bit rate function, H(X i |X j ) is the conditional entropy for X i given X j , and H(X i ,X j ) is the joint entropy.
 
2
The Open SURF project is documented at: http://​code.​google.​com/​p/​opensurf1/​.
 
Literatur
1.
Agarwal, S., Roth, D.: Learning a sparse representation for object detection. In: Proceedings of the European Conference on Computer Vision (2002)
2.
Ailon, N., Chazelle, B.: Approximate nearest neighbors and the fast Johnson–Lindenstrauss transform. In: Proceedings of the ACM Symposium on Theory of Computing (2006)
3.
Bay, H., Ess, A., Tuytelaars, T., Van Gool, L.: SURF: speeded up robust features. Comput. Vis. Image Underst. (2008)
4.
Candès, E., Tao, T.: Near optimal signal recovery from random projections: Universal encoding strategies? IEEE Trans. Inf. Theory (2006)
5.
Chen, D., et al.: Tree histogram coding for mobile image matching. In: Proceedings of the IEEE Data Compression Conference (2009)
6.
Chen, P., et al.: CITRIC: A low-bandwidth wireless camera network platform. In: Proceedings of the International Conference on Distributed Smart Cameras (2008)
7.
Chen, S., Donoho, D., Saunders, M.: Atomic decomposition by basis pursuit. SIAM Rev. (2001)
8.
Christoudias, C., Urtasun, R., Darrell, T.: Unsupervised feature selection via distributed coding for multi-view object recognition. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (2008)
9.
Donoho, D., Tanner, J.: Neighborliness of randomly projected simplices in high dimensions. Proc. Natl. Acad. Sci. (2005)
10.
Duarte, M., et al.: Distributed compressed sensing of jointly sparse signals. In: Proceedings of the Thirty-Ninth Asilomar Conference on Signals, Systems and Computers (2005)
11.
Ferrari, V., Tuytelaars, T., Van Gool, L.: Integrating multiple model views for object recognition. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (2004)
12.
Grauman, K., Darrell, T.: The pyramid match kernel: discriminative classification with sets of image features. In: Proceeding of International Conference on Computer Vision (2005)
13.
Johnson, W., Lindenstrauss, J.: Extensions of Lipschitz maps into a Hilbert space. Contemp. Math. (1984)
14.
Ke, Y., Sukthankar, R.: PCA-SIFT: A more distinctive representation for local image descriptors. In: Proceedings of International Conference on Computer Vision and Pattern Recognition (2004)
15.
Lee, J.: A pyramid match toolkit. MIT CSAIL Tech Report: MUT-CSAIL-TR-2008-017 (2008)
16.
Leibe, B., Seemann, E., Schiele, B.: Pedestrian detection in crowded scenes. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (2005)
17.
Liu, C., et al.: SIFT flow: dense correspondence across different scenes. In: Proceedings of European Conference on Computer Vision (2008)
18.
Lowe, D.: Object recognition from local scale-invariant features. In: Proceedings of International Conference on Computer Vision (1999)
19.
Makar, M., Chang, C., Chen, D., Tsai, S., Girod, B.: Compression on image patches for local feature extraction. In: Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (2009)
20.
Mikolajczyk, K., et al.: A comparison of affine region detectors. Int. J. Comput. Vis. (2005)
21.
Nene, S., Nayar, S., Murase, H.: Columbia object image library (COIL-100). Columbia University Tech Report: CUCS-006-96 (1996)
22.
Nistér, D., Stewénius, H.: Scalable recognition with a vocabulary tree. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (2006)
23.
Plumbley, M.: Recovery of sparse representations by polytope faces pursuit. In: Proceedings of International Conference on Independent Component Analysis and Blind Source Separation (2006)
24.
Quattoni, A., Collins, M., Darrell, T.: Transfer learning for image classification with sparse prototype representations. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (2008)
25.
Slepian, D., Wolf, J.: Noiseless coding of correlated information sources. IEEE Trans. Inf. Theory (1973)
26.
Takacs, G., Chandrasekhar, V., Gelfand, N., Xiong, Y., Chen, W., Bismpigiannis, T., Grzeszczuk, R., Pulli, K., Girod, B.: Outdoor augmented reality on mobile phone using loxel-based visual feature organization. In: Proceedings of International Multimedia Conference (2008)
27.
Thomas, A., et al.: Towards multi-view object class detection. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (2006)
28.
Tibshirani, R.: Regression shrinkage and selection via the LASSO. J. R. Stat. Soc. B (1996)
29.
Weiss, Y., Torralba, A., Fergus, R.: Spectral hashing. In: Proceedings of Advances in Neural Information Processing Systems (2008)
30.
Yang, A., et al.: Distributed compression and fusion of nonnegative sparse signals for multiple-view object recognition. In: Proceedings of International Conference on Information Fusion (2009)
31.
Yeo, C., Ahammad, P., Ramchandran, K.: Rate-efficient visual correspondences using random projections. In: Proceedings of International Conference in Image Processing (2008)
Metadaten
Titel
Multiple-View Object Recognition in Smart Camera Networks
verfasst von
Allen Y. Yang
Subhransu Maji
C. Mario Christoudias
Trevor Darrell
Jitendra Malik
S. Shankar Sastry
Copyright-Jahr
2011
Verlag
Springer London
Buch
Distributed Video Sensor Networks

Print ISBN: 978-0-85729-126-4

Electronic ISBN: 978-0-85729-127-1

Copyright-Jahr: 2011

DOI
https://doi.org/10.1007/978-0-85729-127-1_4