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.
. In general, target variation can be decomposed as short- and long-term variation. @cite_31 proposes a probabilistic mixture model, which has a stable component to account for the long-term variation, a wandering component and a loss component for the short-term variation. Inspired by the Atkinson-Shiffrin Memory Model, @cite_38 uses short- and long-term memory to handle target variations. Correlation filter and keypoint matching are employed for short- and long-term memory, respectively. Instead of using two separate components, we design a single component based on RNN and learn to process short- and long-term information in a data-driven manner.
@cite_38 @cite_31
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Variations in the appearance of a tracked object, such as changes in geometry photometry, camera viewpoint, illumination, or partial occlusion, pose a major challenge to object tracking. Here, we adopt cognitive psychology principles to design a flexible representation that can adapt to changes in object appearance during tracking. Inspired by the well-known Atkinson-Shiffrin Memory Model, we propose MUlti-Store Tracker (MUSTer), a dual-component approach consisting of short- and long-term memory stores to process target appearance memories. A powerful and efficient Integrated Correlation Filter (ICF) is employed in the short-term store for short-term tracking. The integrated long-term component, which is based on keypoint matching-tracking and RANSAC estimation, can interact with the long-term memory and provide additional information for output control. MUSTer was extensively evaluated on the CVPR2013 Online Object Tracking Benchmark (OOTB) and ALOV++ datasets. The experimental results demonstrated the superior performance of MUSTer in comparison with other state-of-art trackers. Standard statistical language models use n-grams to capture local dependencies, or use dynamic modeling techniques to track dependencies within an article. In this paper, we investigate a new statistical language model that captures topic-related dependencies of words within and across sentences. First, we develop a topic-dependent, sentence-level mixture language model which takes advantage of the topic constraints in a sentence or article. Second, we introduce topic-dependent dynamic adaptation techniques in the framework of the mixture model, using n-gram caches and content word unigram caches. Experiments with the static (or unadapted) mixture model on the North American Business (NAB) task show a 21 reduction in perplexity and a 3-4 improvement in recognition accuracy over a general n-gram model, giving a larger gain than that obtained with supervised dynamic cache modeling. Further experiments on the Switchboard corpus also showed a small improvement in performance with the sentence-level mixture model. Cache modeling techniques introduced in the mixture framework contributed a further 14 reduction in perplexity and a small improvement in recognition accuracy on the NAB task for both supervised and unsupervised adaptation. This paper presents Recurrent Policy Gradients, a modelfree reinforcement learning (RL) method creating limited-memory stochastic policies for partially observable Markov decision problems (POMDPs) that require long-term memories of past observations. The approach involves approximating a policy gradient for a Recurrent Neural Network (RNN) by backpropagating return-weighted characteristic eligibilities through time. Using a "Long Short-Term Memory" architecture, we are able to outperform other RL methods on two important benchmark tasks. Furthermore, we show promising results on a complex car driving simulation task. In previous work on "transformed mixtures of Gaussians" and "transformed hidden Markov models", we showed how the EM algorithm in a discrete latent variable model can be used to jointly normalize data (e.g., center images, pitch-normalize spectrograms) and learn a mixture model of the normalized data. The only input to the algorithm is the data, a list of possible transformations, and the number of clusters to find. The main criticism of this work was that the exhaustive computation of the posterior probabilities over transformations would make scaling up to large feature vectors and large sets of transformations intractable. Here, we describe how a tremendous speed-up is acheived through the use of a variational technique for decoupling transformations, and a fast Fourier transform method for computing posterior probabilities. For N × N images, learning C clusters under N rotations, N scales, N x-translations and N y-translations takes only (C + 2 log N)N2 scalar operations per iteration. In contrast, the original algorithm takes CN6 operations to account for these transformations. We give results on learning a 4-component mixture model from a video sequence with frames of size 320×240. The model accounts for 360 rotations and 76,800 translations. Each iteration of EM takes only 10 seconds per frame in MATLAB, which is over 5 million times faster than the original algorithm.