Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1155/2007/64270 | EURASIP J. Adv. Sig. Proc. |
Keywords | Field | DocType |
sensor nodes input,proposed incremental learning,sensor node,cluster head,visual sensor network,incremental support vector machine,online learning,static image-based learning,sensor networks communication,sensor data,initial supervised offline learning,future incremental learning,support vector machine,sensor network | Sensor node,Offline learning,Online machine learning,Computer science,Sensor array,Support vector machine,Sensor fusion,Supervised learning,Artificial intelligence,Wireless sensor network,Machine learning | Journal |
Volume | Issue | ISSN |
2007 | 1 | 1687-6180 |
Citations | PageRank | References |
3 | 0.41 | 14 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mariette Awad | 1 | 104 | 21.39 |
| Xianhua Jiang | 2 | 55 | 6.25 |
| Yuichi Motai | 3 | 230 | 24.68 |