Model update lies at the heart of object tracking. Generally, model update is formulated as an online learning problem, where a target model is learned over the online training set. Our key innovation is to formulate the model update problem in the meta-learning framework and learn the online learning algorithm itself using large numbers of offline videos , i.e., learning to update . The learned updater takes as input the online training set and outputs an updated target model. As a first attempt, we design the learned updater based on recurrent neural networks and demonstrate its application in a template-based tracker and a correlation filter-based tracker. Our learned updater consistently improves the base trackers and runs faster than real time on GPU, while requiring small memory footprint during testing. The experiments on standard benchmarks demonstrate that our learned updater outperforms commonly used update baselines including the efficient exponential moving average-based update and the well-designed stochastic gradient descent-based update. Equipped with our learned updater, the template-based tracker achieves state-of-the-art performance among real-time trackers on GPU.
We propose a novel algorithm to detect occlusion for visual tracking through learning with observation likelihoods. In our technique, target is divided into regular grid cells and the state of occlusion is determined for each cell using a classifier. Each cell in the target is associated with many small patches, and the patch likelihoods observed during tracking construct a feature vector, which is used for classification. Since the occlusion is learned with patch likelihoods instead of patches themselves, the classifier is universally applicable to any videos or objects for occlusion reasoning. Our occlusion detection algorithm has decent performance in accuracy, which is sufficient to improve tracking performance significantly. The proposed algorithm can be combined with many generic tracking methods, and we adopt L1 minimization tracker to test the performance of our framework. The advantage of our algorithm is supported by quantitative and qualitative evaluation, and successful tracking and occlusion reasoning results are illustrated in many challenging video sequences. Recently, on-line adaptation of binary classifiers for tracking have been investigated. On-line learning allows for simple classifiers since only the current view of the object from its surrounding background needs to be discriminiated. However, on-line adaption faces one key problem: Each update of the tracker may introduce an error which, finally, can lead to tracking failure (drifting). The contribution of this paper is a novel on-line semi-supervised boosting method which significantly alleviates the drifting problem in tracking applications. This allows to limit the drifting problem while still staying adaptive to appearance changes. The main idea is to formulate the update process in a semi-supervised fashion as combined decision of a given prior and an on-line classifier. This comes without any parameter tuning. In the experiments, we demonstrate real-time tracking of our SemiBoost tracker on several challenging test sequences where our tracker outperforms other on-line tracking methods. Tracking-by-detection methods have demonstrated competitive performance in recent years. In these approaches, the tracking model heavily relies on the quality of the training set. Due to the limite ... In this paper, we address the problem of tracking an object in a video given its location in the first frame and no other information. Recently, a class of tracking techniques called “tracking by detection” has been shown to give promising results at real-time speeds. These methods train a discriminative classifier in an online manner to separate the object from the background. This classifier bootstraps itself by using the current tracker state to extract positive and negative examples from the current frame. Slight inaccuracies in the tracker can therefore lead to incorrectly labeled training examples, which degrade the classifier and can cause drift. In this paper, we show that using Multiple Instance Learning (MIL) instead of traditional supervised learning avoids these problems and can therefore lead to a more robust tracker with fewer parameter tweaks. We propose a novel online MIL algorithm for object tracking that achieves superior results with real-time performance. We present thorough experimental results (both qualitative and quantitative) on a number of challenging video clips.