Good Features to Correlate for Visual Tracking

During the recent years, correlation filters have shown dominant and spectacular results for visual object tracking. The types of the features that are employed in these family of trackers significantly affect the performance of visual tracking. The ultimate goal is to utilize robust features invariant to any kind of appearance change of the object, while predicting the object location as properly as in the case of no appearance change. As the deep learning based methods have emerged, the study of learning features for specific tasks has accelerated. For instance, discriminative visual tracking methods based on deep architectures have been studied with promising performance. Nevertheless, correlation filter based (CFB) trackers confine themselves to use the pre-trained networks which are trained for object classification problem. To this end, in this manuscript the problem of learning deep fully convolutional features for the CFB visual tracking is formulated. In order to learn the proposed model, a novel and efficient backpropagation algorithm is presented based on the loss function of the network. The proposed learning framework enables the network model to be flexible for a custom design. Moreover, it alleviates the dependency on the network trained for classification. Extensive performance analysis shows the efficacy of the proposed custom design in the CFB tracking framework. By fine-tuning the convolutional parts of a state-of-the-art network and integrating this model to a CFB tracker, which is the top performing one of VOT2016, 18% increase is achieved in terms of expected average overlap, and tracking failures are decreased by 25%, while maintaining the superiority over the state-of-the-art methods in OTB-2013 and OTB-2015 tracking datasets.Comment: Accepted version of IEEE Transactions on Image Processin

Deep Convolutional Correlation Particle Filter for Visual Tracking

In this dissertation, we explore the advantages and limitations of the application of sequential Monte Carlo methods to visual tracking, which is a challenging computer vision problem. We propose six visual tracking models, each of which integrates a particle filter, a deep convolutional neural network, and a correlation filter. In our first model, we generate an image patch corresponding to each particle and use a convolutional neural network (CNN) to extract features from the corresponding image region. A correlation filter then computes the correlation response maps corresponding to these features, which are used to determine the particle weights and estimate the state of the target. We then introduce a particle filter that extends the target state by incorporating its size information. This model also utilizes a new adaptive correlation filtering approach that generates multiple target models to account for potential model update errors. We build upon that strategy to devise an adaptive particle filter that can decrease the number of particles in simple frames in which there is no challenging scenarios and the target model closely reflects the current appearance of the target. This strategy allows us to reduce the computational cost of the particle filter without negatively impacting its performance. This tracker also improves the likelihood model by generating multiple target models using varying model update rates based on the high-likelihood particles. We also propose a novel likelihood particle filter for CNN-correlation visual trackers. Our method uses correlation response maps to estimate likelihood distributions and employs these likelihoods as proposal densities to sample particles. Additionally, our particle filter searches for multiple modes in the likelihood distribution using a Gaussian mixture model. We further introduce an iterative particle filter that performs iterations to decrease the distance between particles and the peaks of their correlation maps which results in having a few more accurate particles in the end of iterations. Applying K-mean clustering method on the remaining particles determine the number of the clusters which is used in evaluation step and find the target state. Our approach ensures a consistent support for the posterior distribution. Thus, we do not need to perform resampling at every video frame, improving the utilization of prior distribution information. Finally, we introduce a novel framework which calculates the confidence score of the tracking algorithm at each video frame based on the correlation response maps of the particles. Our framework applies different model update rules according to the calculated confidence score, reducing tracking failures caused by model drift. The benefits of each of the proposed techniques are demonstrated through experiments using publicly available benchmark datasets

Feature Distilled Tracking

Feature extraction and representation is one of the most important components for fast, accurate, and robust visual tracking. Very deep convolutional neural networks (CNNs) provide effective tools for feature extraction with good generalization ability. However, extracting features using very deep CNN models needs high performance hardware due to its large computation complexity, which prohibits its extensions in real-time applications. To alleviate this problem, we aim at obtaining small and fast-to-execute shallow models based on model compression for visual tracking. Specifically, we propose a small feature distilled network (FDN) for tracking by imitating the intermediate representations of a much deeper network. The FDN extracts rich visual features with higher speed than the original deeper network. To further speed-up, we introduce a shift-and-stitch method to reduce the arithmetic operations, while preserving the spatial resolution of the distilled feature maps unchanged. Finally, a scale adaptive discriminative correlation filter is learned on the distilled feature for visual tracking to handle scale variation of the target. Comprehensive experimental results on object tracking benchmark datasets show that the proposed approach achieves 5x speed-up with competitive performance to the state-of-the-art deep trackers

Deep Motion Features for Visual Tracking

Robust visual tracking is a challenging computer vision problem, with many real-world applications. Most existing approaches employ hand-crafted appearance features, such as HOG or Color Names. Recently, deep RGB features extracted from convolutional neural networks have been successfully applied for tracking. Despite their success, these features only capture appearance information. On the other hand, motion cues provide discriminative and complementary information that can improve tracking performance. Contrary to visual tracking, deep motion features have been successfully applied for action recognition and video classification tasks. Typically, the motion features are learned by training a CNN on optical flow images extracted from large amounts of labeled videos. This paper presents an investigation of the impact of deep motion features in a tracking-by-detection framework. We further show that hand-crafted, deep RGB, and deep motion features contain complementary information. To the best of our knowledge, we are the first to propose fusing appearance information with deep motion features for visual tracking. Comprehensive experiments clearly suggest that our fusion approach with deep motion features outperforms standard methods relying on appearance information alone.Comment: ICPR 2016. Best paper award in the "Computer Vision and Robot Vision" trac