Metrological Aspects in Inteligent Eye-tracking Systems
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Eye-trackers are tools for measuring and recording the movement of the eyeballs. The advancement of technology implies their accuracy increase, which broadens their use in intelligent systems from supporting work, learning to medical diagnostics. The success of using the eye-tracker depends on selecting a device with appropriate metrological parameters. Meanwhile, no objective, standardized measurement methods give repeatable and reproducible test results. Therefore, comparing the equipment available on the market is difficult. To solve this problem, numerous research centres work on appropriate metrics resistant to the technical parameters of the instrument. Another issue is research into hardware properties with human observers. The individual variability of the participants unnecessarily increases the variance of the results. This article discusses both problems and presents a solution that allows to eliminate the human factor from the procedure for determining the metrological properties of the eye-tracker.Keywords:
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Eye-trackers are tools for measuring and recording the movement of the eyeballs. The advancement of technology implies their accuracy increase, which broadens their use in intelligent systems from supporting work, learning to medical diagnostics. The success of using the eye-tracker depends on selecting a device with appropriate metrological parameters. Meanwhile, no objective, standardized measurement methods give repeatable and reproducible test results. Therefore, comparing the equipment available on the market is difficult. To solve this problem, numerous research centres work on appropriate metrics resistant to the technical parameters of the instrument. Another issue is research into hardware properties with human observers. The individual variability of the participants unnecessarily increases the variance of the results. This article discusses both problems and presents a solution that allows to eliminate the human factor from the procedure for determining the metrological properties of the eye-tracker.
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How to combine the complementary capabilities of an ensemble of different algorithms has been of central interest in visual object tracking. A significant progress on such a problem has been achieved, but considering short-term tracking scenarios. Instead, long-term tracking settings have been substantially ignored by the solutions. In this paper, we explicitly consider long-term tracking scenarios and provide a framework, named CoCoLoT, that combines the characteristics of complementary visual trackers to achieve enhanced long-term tracking performance. CoCoLoT perceives whether the trackers are following the target object through an online learned deep verification model, and accordingly activates a decision policy which selects the best performing tracker as well as it corrects the performance of the failing one. The proposed methodology is evaluated extensively and the comparison with several other solutions reveals that it competes favourably with the state-of-the-art on the most popular long-term visual tracking benchmarks.
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Visual tracking in unconstrained environments often involves following an object that exhibits a number of appearance changes from factors such as scale change, rotation, and illumination. Effective tracking requires adapting a tracker to the object's changing appearance over time. When a target becomes occluded by other objects in the scene, a naive tracker may end up learning the appearance of the occluding object. Our work introduces a method of detecting occlusion by considering the color profile of the target to prevent inappropriate tracker updates while the target is occluded. We show improved overlap and central location precision with three visual trackers when adding our hue-based occlusion detection to each tracking system.
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Model-free trackers can track arbitrary objects based on a single (bounding-box) annotation of the object. Whilst the performance of model-free trackers has recently improved significantly, simultaneously tracking multiple objects with similar appearance remains very hard. In this paper, we propose a new multi-object model-free tracker (using a tracking-by-detection framework) that resolves this problem by incorporating spatial constraints between the objects. The spatial constraints are learned along with the object detectors using an online structured SVM algorithm. The experimental evaluation of our structure-preserving object tracker (SPOT) reveals substantial performance improvements in multi-object tracking. We also show that SPOT can improve the performance of single-object trackers by simultaneously tracking different parts of the object. Moreover, we show that SPOT can be used to adapt generic, model-based object detectors during tracking to tailor them towards a specific instance of that object.
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Most existing trackers based on deep learning perform tracking in a holistic strategy, which aims to learn deep representations of the whole target for localizing the target. It is arduous for such methods to track targets with various appearance variations. To address this limitation, another type of methods adopts a part-based tracking strategy which divides the target into equal patches and tracks all these patches in parallel. The target state is inferred by summarizing the tracking results of these patches. A potential limitation of such trackers is that not all patches are equally informative for tracking. Some patches that are not discriminative may have adverse effects. In this paper, we propose to track the salient local parts of the target that are discriminative for tracking. In particular, we propose a fine-grained saliency mining module to capture the local saliencies. Further, we design a saliency-association modeling module to associate the captured saliencies together to learn effective correlation representations between the exemplar and the search image for state estimation. Extensive experiments on five diverse datasets demonstrate that the proposed method performs favorably against state-of-the-art trackers.
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In recent years, Siamese trackers have been extensively studied because of their high accuracy and speed. However, when the target is occluded by other objects, the result will be greatly drifted, which affects the quality of the tracking results. This study based on the RGB-D data proposes an object tracking method integrating a target occlusion estimation module and a target location correction module, called Siamese-Occlusion-Correction (SiamOC). When a target is occluded, these modules can help the Siamese tracker correct the target location. In this paper, experiments demonstrate that the method which is the real-time tracker has achieved competitive results on the CDTB dataset.
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Visual tracking plays an important role in many computer vision tasks. A common assumption in previous methods is that the video frames are blur free. In reality, motion blurs are pervasive in the real videos. In this paper we present a novel BLUr-driven Tracker (BLUT) framework for tracking motion-blurred targets. BLUT actively uses the information from blurs without performing debluring. Specifically, we integrate the tracking problem with the motion-from-blur problem under a unified sparse approximation framework. We further use the motion information inferred by blurs to guide the sampling process in the particle filter based tracking. To evaluate our method, we have collected a large number of video sequences with significant motion blurs and compared BLUT with state-of-the-art trackers. Experimental results show that, while many previous methods are sensitive to motion blurs, BLUT can robustly and reliably track severely blurred targets.
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Target tracking using color based appearance models is very popular in visual tracking. However, trackers based only on color are fragile and often drift to the background when it has similar appearances. In this paper, we propose an efficient way to use distinctive target colors to track the target and eliminate the drift problem. Colors are sampled from the target and its immediate surrounding region. And color samples coming from target result in more distinctive target color. In our approach, we use a short and a long time color histogram to represent the target color. The short time color histogram is used to calculate the distinctiveness of colors while the long time color histogram is used to keep the target color that is consistent over time. In our approach, the target is not marked as a rectangle or other geometric primitives, instead, we track it with its own silhouette. Using silhouette to mark target significantly reduces the false positive information during online learning. Also, the color models are updated with a dynamic learning factor which is based on the tracking result. After testing with many tracking sequences and comparison with other state-of-art trackers, the proposed tracking algorithm shows comparably better performance with very high tracking rate.
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Currently, eye trackers can estimate with high precision using eye images. This method requires high-quality eye images, and thus cannot estimate using low-quality eye images, which have low resolution, illumination changes, and occlusions. Other methods use pose information, including head direction, body direction, and head motion. This method obtains pose information using the Eigenspace method, and thus requires a calibration phase. In addition, the eye direction using this method is only horizontal. We propose a gaze estimation algorithm that does not require high quality eye images. The purpose is estimation without using eye images, with an error of less than ten degrees. Experimental results show that the method can estimate gaze direction without using eye images.
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