Improved Face Tracking Thanks to Local Features Correspondence

In this paper, we propose a technique to enhance the quality of detected face tracks in videos. In particular, we present a tracking algorithm that can improve the temporal localization of the tracks, remedying to the unavoidable failures of the face detection algorithms. Local features are extracted and tracked to “fill the gaps” left by missed detections. The principal aim of this work is to provide robust and well localized tracks of faces to a system of Interactive Movietelling, but the concepts can be extended whenever there is the necessity to localize the presence of a determined face even in environments where the face detection is, for any reason, difficult. We test the effectiveness of the proposed algorithm in terms of faces localization both in space and time, first assessing the performance in an ad-hoc simulation scenario and then showing output examples of some real-world video sequences

Tracking icebergs with time-lapse photography and sparse optical flow, LeConte Bay, Alaska, 2016–2017

We present a workflow to track icebergs in proglacial fjords using oblique time-lapse photos and the Lucas-Kanade optical flow algorithm. We employ the workflow at LeConte Bay, Alaska, where we ran five time-lapse cameras between April 2016 and September 2017, capturing more than 400 000 photos at frame rates of 0.5–4.0 min−1. Hourly to daily average velocity fields in map coordinates illustrate dynamic currents in the bay, with dominant downfjord velocities (exceeding 0.5 m s−1 intermittently) and several eddies. Comparisons with simultaneous Acoustic Doppler Current Profiler (ADCP) measurements yield best agreement for the uppermost ADCP levels (∼ 12 m and above), in line with prevalent small icebergs that trace near-surface currents. Tracking results from multiple cameras compare favorably, although cameras with lower frame rates (0.5 min−1) tend to underestimate high flow speeds. Tests to determine requisite temporal and spatial image resolution confirm the importance of high image frame rates, while spatial resolution is of secondary importance. Application of our procedure to other fjords will be successful if iceberg concentrations are high enough and if the camera frame rates are sufficiently rapid (at least 1 min−1 for conditions similar to LeConte Bay).This work was funded by the U.S. National Science Foundation (OPP-1503910, OPP-1504288, OPP-1504521 and OPP-1504191).Ye

Video-based assistance system for training in minimally invasive surgery

In this paper, the development of an assisting system for laparoscopic surgical training is presented. With this system, we expect to facilitate the training process at the first stages of training in laparoscopic surgery and to contribute to an objective evaluation of surgical skills. To achieve this, we propose the insertion of multimedia contents and outlines of work adapted to the level of experience of trainees and the detection of the movements of the laparoscopic instrument into the monitored image. A module to track the instrument is implemented focusing on the tip of the laparoscopic tool. This tracking method does not need the presence of artificial marks or special colours to distinguish the instruments. Similarly, the system has another method based on visual tracking to localize support multimedia content in a stable position of the field of vision. Therefore, this position of the support content is adapted to the movements of the camera or the working area. Experimental results are presented to show the feasibility of the proposed system for assisting in laparoscopic surgical training

GAZE ESTIMATION USING SCLERA AND IRIS EXTRACTION

Tracking gaze of an individual provides important information in understanding the behavior of that person. Gaze tracking has been widely used in a variety of applications from tracking consumers gaze fixation on advertisements, controlling human-computer devices, to understanding behaviors of patients with various types of visual and/or neurological disorders such as autism. Gaze pattern can be identified using different methods but most of them require the use of specialized equipments which can be prohibitively expensive for some applications. In this dissertation, we investigate the possibility of using sclera and iris regions captured in a webcam sequence to estimate gaze pattern. The sclera and iris regions in the video frame are first extracted by using an adaptive thresholding technique. The gaze pattern is then determined based on areas of different sclera and iris regions and distances between tracked points along the irises. The technique is novel as sclera regions are often ignored in eye tracking literature while we have demonstrated that they can be easily extracted from images captured by low-cost camera and are useful in determining the gaze pattern. The accuracy and computational efficiency of the proposed technique is demonstrated by experiments with human subjects

Temporal unpredictability detection of real-time video sequence

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