SSOCBT: A Robust Semisupervised Online CovBoost Tracker That Uses Samples Differently

Abstract

Most existing feature selection methods for object tracking assume that the samples in the previous frames are governed by the same distribution of the labeled samples obtained in the current frame and unlabeled samples collected in the next frame. However, according to our statistical analysis on very common videos, this assumption is not true in many scenarios. As a result, the selected features are not suitable for discriminating between the target from the background in the next frame. A tracking error accumulates and finally the drift problem happens. In this paper, we consider data distribution in tracking from a new perspective to adapt to target's and background's changes. We classify the samples into three categories: auxiliary samples (samples in the previous frames), target samples (samples collected in the current frame), and unlabeled samples (samples obtained in the next frame). To make the best use of them for tracking, we propose a novel semisupervised transfer learning approach that treats samples differently. Specifically, we assume that only target samples follow the same distribution as the unlabeled samples that we want to classify. Then, a novel and interesting semisupervised CovBoost method is developed utilizing the information provided by the three kinds of samples effectively when training the best strong classifier for tracking. Furthermore, we develop a new online updating algorithm for semisupervised CovBoost, making our tracker handle with significant variations of the tracked target and background successfully. Our experimental results demonstrate the advantages of treating samples differently during tracking. Our tracker outperforms state-of-the-art trackers on the benchmark datasets.