- Research Article
- Open Access
Incremental Support Vector Machine Framework for Visual Sensor Networks
EURASIP Journal on Advances in Signal Processing volume 2007, Article number: 064270 (2006)
Motivated by the emerging requirements of surveillance networks, we present in this paper an incremental multiclassification support vector machine (SVM) technique as a new framework for action classification based on real-time multivideo collected by homogeneous sites. The technique is based on an adaptation of least square SVM (LS-SVM) formulation but extends beyond the static image-based learning of current SVM methodologies. In applying the technique, an initial supervised offline learning phase is followed by a visual behavior data acquisition and an online learning phase during which the cluster head performs an ensemble of model aggregations based on the sensor nodes inputs. The cluster head then selectively switches on designated sensor nodes for future incremental learning. Combining sensor data offers an improvement over single camera sensing especially when the latter has an occluded view of the target object. The optimization involved alleviates the burdens of power consumption and communication bandwidth requirements. The resulting misclassification error rate, the iterative error reduction rate of the proposed incremental learning, and the decision fusion technique prove its validity when applied to visual sensor networks. Furthermore, the enabled online learning allows an adaptive domain knowledge insertion and offers the advantage of reducing both the model training time and the information storage requirements of the overall system which makes it even more attractive for distributed sensor networks communication.
Zhao F: Challenges in designing information sensor processing networks. Talk at NSF Workshop on Networking of Sensor Systems, February 2004, Marina Del Ray, Calif, USA
Chong C-Y, Kumar SP: Sensor networks: evolution, opportunities, and challenges. Proceedings of the IEEE 2003,91(8):1247–1256. 10.1109/JPROC.2003.814918
Akyildiz IF, Su W, Sankarasubramaniam Y, Cayirci E: A survey on sensor networks. IEEE Communications Magazine 2002,40(8):102–105. 10.1109/MCOM.2002.1024422
Duda R, Hart P, Stock D: Pattern Classification. 2nd edition. John Willy & Sons, New York, NY, USA; 2001.
Hampapur A, Brown L, Connell J, Pankanti S, Senior A, Tian Y: Smart surveillance: applications, technologies and implications. Proceedings of the 4th International Conference on the Communications and Signal Processing, and the 4th Pacific Rim Conference on Multimedia, December 2003, Singapore, Republic of Singapore 2: 1133–1138.
Haritaoglu I, Flickner M: Detection and tracking of shopping groups in stores. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '01), December 2001, Kauai, Hawaii, USA 1: 431–438.
Zurn JB, Hohmann D, Dworkin SI, Motai Y: A real-time rodent tracking system for both light and dark cycle behavior analysis. Proceedings of the IEEE Workshop on Applications of Computer Vision, January 2005, Breckenridge, Colo, USA 87–92.
Cristianini N, Shawe-Taylor J: An Introduction to Support Vector Machines and Other kernel-Based Learning Methods. Cambridge University Press, Cambridge, Mass, USA; 2000.
Vapnik V, Mukherjee S: Support vector method for multivariant density estimation. Advances in Neural Information Processing Systems (NIPS '99), November–December 1999, Denver, Colo, USA 659–665.
Herbrich R, Graepel T, Campbell C: Bayes point machines: estimating the Bayes point in kernel space. Proceedings of International Joint Conference on Artificial Intelligence Workshop on Support Vector Machines (IJCAI '99), July–August 1999, Stockholm, Sweden 23–27.
Platt J: Fast training of support vector machines using sequential minimal optimization. In Advances in Kernel Methods-Support Vector Learning. MIT Press, Cambridge, Mass, USA; 1999:185–208.
Suykens JAK, Vandewalle J: Least squares support vector machine classifiers. Neural Processing Letters 1999,9(3):293–300. 10.1023/A:1018628609742
Schölkopf B, Smola AJ: Learning with Kernels. MIT Press, Cambridge, Mass, USA; 2002.
Ralaivola L, d'Alch'e-Buc F: Incremental support vector machine learning: a local approach. Proceedings of the International Conference on Artificial Neural Networks (ICANN '01), August 2001, Vienna, Austria 322–330.
Cauwenberghs G, Poggio T: Incremental and decremental support vector machine learning. Advances in Neural Information Processing Systems (NIPS '00), December 2000, Denver, Colo, USA 409–415.
Forsyth DA, Ponce J: Computer Vision: A Modern Approach. Prentice Hall, Upper Saddle River, NJ, USA; 2003.
Hsu C-W, Lin C: A comparison of methods for multiclass support vector machines. IEEE Transactions on Neural Networks 2002,13(2):415–425. 10.1109/72.991427
Matrix algebra; https://doi.org/www.ec-securehost.com/SIAM/ot71.html
Heinzelman WR, Chandrakasan A, Balakrishnan H: Energy-efficient communication protocol for wireless microsensor networks. Proceedings of the 33rd Annual Hawaii International Conference on System Sciences (HICSS '33), January 2000, Maui, Hawaii, USA 2: 10.
Antony R: Principles of Data Fusion Automation. Artech House, Boston, Mass, USA; 1995.
Newsan S, Testic J, Wang L, Manjunah BS: Issues in managing image and video data. Storage and Retrieval Methods and Applications for Multimedia, January 2004, San Jose, Calif, USA, Proceedings of SPIE 5307: 280–291.
Zelikovitz S: Mining for features to improve classification. Proceedings of the International Conference on Machine Learning; Models, Technologies and Applications (MLMTA '03), June 2003, Las Vegas, Nevada, USA 108–114.
Trivedi MM, Mikic I, Kogut G: Distributed video networks for incident detection and management. Proceedings of IEEE Conference on Intelligent Transportation Systems (ITSC '00), October 2000, Dearborn, Mich, USA 155–160.
Matsuyama T, Hiura S, Wada T, Murase K, Yoshioka A: Dynamic memory: architecture for real time integration of visual perception, camera action, and network communication. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR '00), June 2000, Hilton Head Island, SC, USA 2: 728–735.
Haritaoglu I, Harwood D, Davis LS:: a real time system for detecting and tracking people. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, June 1998, Santa Barbara, Calif, USA 962–962.
Nakazawa A, Kato H, Inokuchi S: Human tracking using distributed vision systems. Proceedings of 14th International Conference on Pattern Recognition, August 1998, Brisbane, Australia 1: 593–596.
Sogo T, Ishiguro H, Trivedi MM: N-ocular stereo for real-time human tracking. In Panoramic Vision: Sensors, Theory and Applications. Springer, New York, NY, USA; 2000.
Al-Ani A, Deriche M: A new technique for combining multiple classifiers using the Dempster-Shafer theory of evidence. Journal of Artificial Intelligence Research 2002, 17: 333–361.
Jiang X, Motai Y: Incremental on-line PCA for automatic motion learning of eigen behavior. special issue of automatic learning and real-time, to appear in International Journal of Intelligent Systems Technologies and Applications
Belongie S, Malik J, Puzicha J: Shape matching and object recognition using shape contexts. IEEE Transactions on Pattern Analysis and Machine Intelligence 2002,24(4):509–522. 10.1109/34.993558
Watanachaturaporn P, Arora MK: SVM for classification of multi—and hyperspectral data. In Advanced Image Processing Techniques for Remotely Sensed Hyperspectral Data. Edited by: Varshney PK, Arora MK. Springer, New York, NY, USA; 2004.
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Awad, M., Jiang, X. & Motai, Y. Incremental Support Vector Machine Framework for Visual Sensor Networks. EURASIP J. Adv. Signal Process. 2007, 064270 (2006). https://doi.org/10.1155/2007/64270
- Support Vector Machine
- Sensor Node
- Cluster Head
- Incremental Learning
- Error Reduction Rate