Name
Abstract | ||
|---|---|---|
Classifying moving objects to semantically meaningful categories is important for automatic visual surveillance. However, this is a challenging problem due to the factors related to the limited object size, large intra-class variations of objects in a same class owing to different viewing angles and lighting, and real-time performance requirement in real-world applications. This paper describes an appearance-based method to achieve real-time and robust objects classification in diverse camera viewing angles. A new descriptor, i.e., the multi-block local binary pattern (MB-LBP), is proposed to capture the large-scale structures in object appearances. Based on MB-LBP features, an adaBoost algorithm is introduced to select a subset of discriminative features as well as construct the strong two-class classifier. To deal with the non-metric feature value of MB-LBP features, a multi-branch regression tree is developed as the weak classifiers of the boosting. Finally, the error correcting output code (ECOC) is introduced to achieve robust multi-class classification performance. Experimental results show that our approach can achieve real-time and robust object classification in diverse scenes. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1109/CVPR.2007.383503 | CVPR |
Keywords | Field | DocType |
multiblock local binary pattern,adaboost algorithm,real-time object classification,trees (mathematics),learning (artificial intelligence),moving object classification,regression analysis,appearance-based method,error correction codes,image classification,error correcting output code,multibranch regression tree,appearance learning,video surveillance,image motion analysis,real time,layout,local binary pattern,object recognition,learning artificial intelligence,shape,regression tree,multi class classification,robustness | Decision tree,Computer vision,Pattern recognition,Computer science,Local binary patterns,Robustness (computer science),Boosting (machine learning),Artificial intelligence,Contextual image classification,Classifier (linguistics),Discriminative model,Cognitive neuroscience of visual object recognition | Conference |
Volume | Issue | ISSN |
2007 | 1 | 1063-6919 E-ISBN : 1-4244-1180-7 |
ISBN | Citations | PageRank |
1-4244-1180-7 | 26 | 1.11 |
References | Authors | |
23 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lun Zhang | 1 | 635 | 28.46 |
| Stan Z. Li | 2 | 8951 | 535.26 |
| Xiao-Tong Yuan | 3 | 792 | 49.95 |
| Shiming Xiang | 4 | 2136 | 110.53 |