Visual tracking concerns the problem of following an arbitrary object in a video sequence. In this thesis, we examine how to use stereo images to extend existing visual tracking algorithms, which methods exists to obtain information from stereo images, and how the results change as the parameters to each tracker vary. For this purpose, four abstract approaches are identified, with five distinct implementations. Each tracker implementation is an extension of a baseline algorithm, MOSSE. The free parameters of each model are optimized with respect to two different evaluation strategies called nor- and wir-tests, and four different objective functions, which are then fixed when comparing the models against each other. The results are created on single target tracks extracted from the KITTI tracking dataset, and the optimization results show that none of the objective functions are sensitive to the exposed parameters under the joint selection of model and dataset. The evaluation results also shows that none of the extensions improve the results of the baseline tracker.

Visual Object Tracking is the computer vision problem of estimating a target trajectory in a video given only its initial state. A visual tracker often acts as a component in the intelligent vision systems seen in for instance surveillance, autonomous vehicles or robots, and unmanned aerial vehicles. Applications may require robust tracking performance on difficult sequences depicting targets undergoing large changes in appearance, while enforcing a real-time constraint. Discriminative correlation filters have shown promising tracking performance in recent years, and consistently improved state-of-the-art. With the advent of deep learning, new robust deep features have improved tracking performance considerably. However, methods based on discriminative correlation filters learn a rigid template describing the target appearance. This implies an assumption of target rigidity which is not fulfilled in practice. This thesis introduces an approach which integrates deformability into a stateof-the-art tracker. The approach is thoroughly tested on three challenging visual tracking benchmarks, achieving state-of-the-art performance.

Visual object tracking is a classical and very popular problem in computer vision with a plethora of applications such as vehicle navigation, human computer interface, human motion analysis, surveillance, auto-control systems and many more. Given the initial state of a target in the first frame, the goal of tracking is to predict states of the target over time where the states describe a bounding box covering the target. Despite numerous object tracking methods that have been proposed in recent years [1-4], most of these trackers suffer a degradation in performance mainly because of several challenges that include illumination changes, motion blur, complex motion, out of plane rotation, and partial or full occlusion, while occlusion is usually the most contributing factor in degrading the majority of trackers, if not all of them. This thesis is devoted to the advancement of generic object trackers tackling different challenges through different proposed methods. The work presented propose four new state-of-the-art trackers. One of which is 3D based tracker in a particle filter framework where both synchronization and registration of RGB and depth streams are adjusted automatically, and three works in correlation filters that achieve state-of-the-art performance in terms of accuracy while maintaining reasonable speeds.

Generic visual object tracking is the task of tracking one or several objects in all frames in a video, knowing only the location and size of the target in the initial frame. Visual tracking can be carried out in both the infrared and the visual spectrum simultaneously, this is known as multi-modal tracking. Utilizing both spectra can result in a more diverse tracker since visual tracking in infrared imagery makes it possible to detect objects even in poor visibility or in complete darkness. However, infrared imagery lacks the number of details that are present in visual images. A common method for visual tracking is to use discriminative correlation filters (DCF). These correlation filters are then used to detect an object in every frame of an image sequence. This thesis focuses on investigating aspects of a DCF based tracker, operating in the two different modalities, infrared and visual imagery. First, it was investigated whether the tracking benefits from using two channels instead of one and what happens to the tracking result if one of those channels is degraded by an external cause. It was also investigated if the addition of image features can further improve the tracking. The result shows that the tracking improves when using two channels instead of only using a single channel. It also shows that utilizing two channels is a good way to create a robust tracker, which is still able to perform even though one of the channels is degraded. Using deep features, extracted from a pre-trained convolutional neural network, was the image feature improving the tracking the most, although the implementation of the deep features made the tracking significantly slower.

The mathematical operation of correlation is a very simple concept, yet has a very rich history of application in a variety of engineering fields. It is essentially nothing but a technique to measure if and to what degree two signals match each other. Since this is a very basic and universal task in a wide variety of fields such as signal processing, communications, computer vision etc., it has been an important tool. The field of pattern recognition often deals with the task of analyzing signals or useful information from signals and classifying them into classes. Very often, these classes are predetermined, and examples (templates) are available for comparison. This task naturally lends itself to the application of correlation as a tool to accomplish this goal. Thus the field of Correlation Pattern Recognition has developed over the past few decades as an important area of research. From the signal processing point of view, correlation is nothing but a filtering operation. Thus there has been a great deal of work in using concepts from filter theory to develop Correlation Filters for pattern recognition. While considerable work has been to done to develop linear correlation filters over the years, especially in the field of Automatic Target Recognition, a lot of attention has recently been paid to the development of Quadratic Correlation Filters (QCF). QCFs offer the advantages of linear filters while optimizing a bank of these simultaneously to offer much improved performance. This dissertation develops efficient QCFs that offer significant savings in storage requirements and computational complexity over existing designs. Firstly, an adaptive algorithm is presented that is able to modify the QCF coefficients as new data is observed. Secondly, a transform domain implementation of the QCF is presented that has the benefits of lower computational complexity and computational requirements while retaining excellent recognition accuracy. Finally, a two dimensional QCF is presented that holds the potential to further save on storage and computations. The techniques are developed based on the recently proposed Rayleigh Quotient Quadratic Correlation Filter (RQQCF) and simulation results are provided on synthetic and real datasets.

Generic visual tracking is a challenging computer vision problem, where the position of a specified target is estimated through a sequence of frames. The only given information is the initial location of the target. Therefore, the tracker has to adapt and learn any kind of object, which it describes through visual features used to differentiate target from background. Standard appearance features only capture momentary visual information. This master’s thesis investigates the use of deep features extracted through optical flow images processed in a deep convolutional network. The optical flow is calculated using two consecutive images, and thereby captures the dynamic nature of the scene. Results show that this information is complementary to the standard appearance features, and improves performance of the tracker. Deep features are typically very high dimensional. Employing dimensionality reduction can increase both the efficiency and performance of the tracker. As a second aim in this thesis, PCA and PLS were evaluated and compared. The evaluations show that the two methods are almost equal in performance, with PLS actually receiving slightly better score than the popular PCA. The final proposed tracker was evaluated on three challenging datasets, and was shown to outperform other state-of-the-art trackers.

Object tracking can be done in numerous ways, where the goal is to track a target through all frames in a sequence. The ground truth bounding box is used to initialize the object tracking algorithm. Object tracking can be carried out on infrared imagery suitable for military applications to execute tracking even without illumination. Objects, such as aircraft, can deploy countermeasures to impede tracking. The countermeasures most often mainly impact one wavelength band. Therefore, using two different wavelength bands for object tracking can counteract the impact of the countermeasures. The dataset was created from simulations. The countermeasures applied to the dataset are flares and Directional Infrared Countermeasures (DIRCMs). Different object tracking algorithms exist, and many are based on discriminative correlation filters (DCF). The thesis investigated the DCF-based trackers STRCF and ECO on the created dataset. The STRCF and the ECO trackers were analyzed using one and two wavelength bands. The following features were investigated for both trackers: grayscale, Histogram of Oriented Gradients (HOG), and pre-trained deep features. The results indicated that the STRCF and the ECO trackers using two wavelength bands instead of one improved performance on sequences with countermeasures. The use of HOG, deep features, or a combination of both improved the performance of the STRCF tracker using two wavelength bands. Likewise, the performance of the ECO tracker using two wavelength bands was improved by the use of deep features. However, the negative aspect of using two wavelength bands and introducing more features is that it resulted in a lower frame rate.