Application of gcForest to visual tracking using UAV image sequences

Visual object tracking is a core technology and challenging research in computer vision. With the rapid development of unmanned aerial vehicle (UAV), more and more UAVs are equipped with video cameras to conduct object tracking. Researching object tracking of UAV image sequences is of great help for practical applications. Recent trackers based on deep networks have a great success by learning an effective representation of targets, however, the offline training is time-consuming, and the complex network may reduce its efficiency. In this paper, to effectively decrease the training time and handle the problem of network complexity, an effective tracking algorithm called multi-scale gcForest tracking (MSGCF) is proposed. First, to enrich the sample information and overcome the problem of scale variations, the multi-scale transformation is used for helping network to obtain the stronger representation of target samples. Then, gcForest that is a decision tree ensemble approach is utilized in tracking task to extract the target features, and gcForest is much easier to train and works well in small-scale training data. Most importantly, we make improvements to the gcForest by adding three channels at the top of network to adapt to multi-scale images and improve the representation ability of network. Furthermore, computation speed can be increased greatly through the method of reducing feature dimension based on compressive sensing (CS) theory, and the power of features can also be reserved. Finally, a support vector machine (SVM) classifier is employed to effectively separate targets and backgrounds. Extensive experimental results on UAV image sequences and challenging benchmark data sets demonstrate that the proposed MSGCF algorithm is effective.