Visual tracking is one of the most active computer vision research topics because of its wide range of applications. Currently

target tracking problems are often solved through online learning and detection methods. A tracking task can be considered a binary classification problem solved using online learning method. However

in the process of online learning

the classifier training takes a considerable amount of time to improve its recognition accuracy. In this study

a method using the Adaboost algorithm is proposed to solve this problem. The algorithm initially trains weak classifiers in a certain number of beginning frames and will subsequently perform only as a detector without training to address the issues related to real time and accuracy. The Haar feature needs to be simplified because its computational cost remains a burden for real-time tracking. Thus

we remove the Haar orientation to facilitate calculation. Positive samples

i.e.

samples containing the target

are always the minority in tracking; as a result

the training samples are imbalanced. Accordingly

the algorithm needs to focus more on the positive targets to achieve higher detection rate. The equal treatment of false positives and false negatives by Adaboost may no longer be appropriate. In this case

we choose a cost-sensitive Adaboost to achieve higher detection rate for the positives. Furthermore

given that misclassified samples appear more often during a scenario because of the complex environment in visual tracking

we add a new parameter in the sample weight-updating formula of the cost-sensitive Adaboost to provide more weight to the misclassified samples

which consequently will be given more focus by the classifier. Finally

we propose a tracking method based on the simplified Haar feature as descriptor and the improved cost-sensitive Adaboost as classifier with online learning strategy. In our experiments

we compared our method with two state-of-the-art algorithms and the original cost-sensitive method in both accuracy and processing speed. We tested the different methods on 20 benchmark video sequences. In terms of accuracy

the average representative precision of our method is approximately 26% higher than that of the compressive tracking method and approximately 11% higher than that of the original cost-sensitive method. In terms of processing speed

the average frame rate of our method is approximately 38% faster than that of the compressive tracking method. Our method is based on a modified cost-sensitive Adaboost that focuses more on the minority positive samples to improve detection rate. The proposed method performs well in terms of accuracy and speed

especially for non-rigid objects

such as human bodies.