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Improving Head Detection from Tracking

In this paper, we propose a novel framework for head detection and tracking in video sequences. At first, an off-line classifier is trained with a few labeled samples. And it was used to object detection in video sequences. Based on online boosting algorithm, the detected objects will be used to train the classifier as new samples. Instead of using another detection algorithm to label the new sample automatically like other online boosting framework, we ensure the correct label from tracking. Furthermore, the weights of new samples can be obtained from tracking directly. Thus the training speed of the classifier can be improved. Experimental results on two video datasets are provided to show the efficient and high detection rate of the framework. In this paper, we propose a novel framework based on online boosting. A detector is first trained off-line with a few labeled samples. When an object is detected by the offline learned object detector, it will be used to train the detector as new positive sample or negative sample based on online boosting. In order to ensure the correct label automatically, the object will be verified by tracking. We will complete the track based on the maximum a posteriori estimate. And the posterior probability can be used to determine credibility coefficients of detected objects. Thus the detected object will be used to train the detector as a new positive sample or negative sample. Furthermore, the on-line boosting system uses the surrounding background as negative examples. It will improve tracking algorithm against the drifting problem. Therefore both the tracking and the detection algorithms help improve the performance of each other.