Predictive Visual Tracking: A New Benchmark and Baseline Approach
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As a crucial robotic perception capability, visual tracking has been intensively studied recently. In the real-world scenarios, the onboard processing time of the image streams inevitably leads to a discrepancy between the tracking results and the real-world states. However, existing visual tracking benchmarks commonly run the trackers offline and ignore such latency in the evaluation. In this work, we aim to deal with a more realistic problem of latency-aware tracking. The state-of-the-art trackers are evaluated in the aerial scenarios with new metrics jointly assessing the tracking accuracy and efficiency. Moreover, a new predictive visual tracking baseline is developed to compensate for the latency stemming from the onboard computation. Our latency-aware benchmark can provide a more realistic evaluation of the trackers for the robotic applications. Besides, exhaustive experiments have proven the effectiveness of the proposed predictive visual tracking baseline approach.Keywords:
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Though not often mentioned, the price point of many eye tracking systems may be a factor limiting their adoption in research. Recently, several inexpensive eye trackers have appeared on the market, but to date little systematic research has been conducted to validate these systems. The present experiment attempted to address this gap by evaluating and comparing five different eye trackers, the Eye Tribe Tracker, Tobii EyeX, Seeing Machines faceLAB, Smart Eye Pro, and Smart Eye Aurora for their gaze tracking accuracy and precision. Results suggest that all evaluated trackers maintained acceptable accuracy and precision, but lower cost systems frequently also experienced high rates of data loss, suggesting that researchers adopting low cost systems such as those evaluated here should be judicious in their research usage.
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Various eye-trackers have recently become commercially available, but studies on more high-spec eye-tracking system have been conducted. Especially, studies have shown that conventional eye-trackers are rather inflexible in layout. The cameras employed in these eye-trackers, as well as the light sources and user's position are fixed, and only a predefined plane can be the target of the eye-tracking. In this study, we propose a new framework that we call a Free-Target Eye-tracking System, which consists of eye-tracking hardware and a hardware layout solver. We developed a prototype of a hardware layout solver and demonstrated its effectiveness.
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Eye tracking is a compelling tool for revealing people's spatial-temporal distribution of visual attention. But quality eye tracking hardware is expensive and can only be used with one person at a time. Further, webcam eye tracking systems have significant limitations on head movement and lighting conditions that result in significant data loss and inaccuracies. To address these drawbacks, we introduce a new approach that harnesses the crowd to understand allocation of visual attention. In our approach, crowdsourcing participants use mouse clicks to self-report the positions and trajectory for the following valuable eye tracking measures: first gaze, last gaze and all gazes. We validate our crowdsourcing approach with a user study, which demonstrated good accuracy when compared to a real eye tracker. We then deployed our prototype, GazeCrowd, in a crowdsourcing setting, and showed that it accurately generated gaze heatmaps and trajectory maps. Such an approach will allow designers to evaluate and refine their visual design without requiring the use of limited/expensive eye trackers.
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Eye Gaze Tracking is emerging as a popular method to increase the efficiency as well as to increase the accessibility of computing systems. It has been already employed in various applications as well as many researches are going on to incorporate the concept in various other fields. There are various fields where the systems are employed ranging from ease of accessibility of computing systems [1] to performance of computer graphics processing by the method of foveated rendering [2] and as well as digital marketing analysis [3]. The application of eye gaze trackers is still limited due to its lack of availability as well as the cost of the eye gaze trackers which makes them still a scarce resource for majority of the population. This paper proposes an efficient system for Eye Gaze Tracking and draws a sharp contrast between the different open source software available for Eye Gaze Tracking on the market. The study draws the comparison between two of the most popular open source eye gaze tracking software available in the market. ITU Gaze Tracker [4] PyGame based Eye Tracker [5]. The components proposed for the system are easily available in the market.
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The development of eye tracking-based applications has witnessed a number of advancements over the past few years. As a result, a number of low cost commercial remote vision-based eye trackers started to appear in the market. Consequently, a number of research communities started to explore the feasibility of extending the eye-tracking capabilities beyond single computer screen and utilize it in multi-screen setup. One of the main challenges for the wide adoption of such eye trackers in multi-screen setup, is their limitations when it comes to an intuitive and reliable way for tracking human eye movements across these multiple screens without losing much of the eye tracking data itself. In this work, a novel data-driven approach based on deep recurrent neural networks for a reliable and responsive switching mechanism between low cost multi-screen eye trackers is proposed. Our approach has achieved a competent results in terms of higher accuracy and lower positive rate in detecting accurately the screen the subject is attending to with F1 measure score of 85%.
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