Online Detection and Classification of Moving Objects Using Progressively Improving Detectors

Boosting based detection methods have successfully been used for robust detection of faces and pedestrians. However, a very large amount of labeled examples are required for training such a classifier. Moreover, once trained, the boosted classifier cannot adjust to the particular scenario in which it is employed. In this paper, we propose a co-training based approach to continuously label incoming data and use it for online update of the boosted classifier that was initially trained from a small labeled example set. The main contribution of our approach is that it is an online procedure in which separate views (features) of the data are used for co-training, while the combined view (all features) is used to make classification decisions in a single boosted framework. The features used for classification are derived from Principal Component Analysis of the appearance templates of the training examples. In order to speed up the classification, background modeling is used to prune away s...

• Type: Conference
• Year: 2005
• Where: CVPR
• Authors: Omar Javed, Saad Ali, Mubarak Shah
• Keywords: Co-training Based Approach, Computer Vision, CVPR 2005, Principal Component Analysis, Significant Performance Gains, Single Boosted Framework, Small Labeled Example