Driving saccade to pursuit using image motion

Within the context of active vision, scant attention has been paid to the execution of motion saccades—rapid re-adjustments of the direction of gaze to attend to moving objects. In this paper we first develop a methodology for, and give real-time demonstrations of, the use of motion detection and segmentation processes to initiate capture saccades towards a moving object. The saccade is driven by both position and velocity of the moving target under the assumption of constant target velocity, using prediction to overcome the delay introduced by visual processing. We next demonstrate the use of a first order approximation to the segmented motion field to compute bounds on the time-to-contact in the presence of looming motion. If the bound falls below a safe limit, a panic saccade is fired, moving the camera away from the approaching object. We then describe the use of image motion to realize smooth pursuit, tracking using velocity information alone, where the camera is moved so as to null a single constant image motion fitted within a central image region. Finally, we glue together capture saccades with smooth pursuit, thus effecting changes in both what is being attended to and how it is being attended to. To couple the different visual activities of waiting, saccading, pursuing and panicking, we use a finite state machine which provides inherent robustness outside of visual processing and provides a means of making repeated exploration. We demonstrate in repeated trials that the transition from saccadic motion to tracking is more likely to succeed using position and velocity control, than when using position alone.

Transfer of conceptualisation patterns in bilinguals: the construal of motion events in Turkish and German

We test Slobin's (2003) Thinking-for-Speaking hypothesis on data from different groups of Turkish-German bilinguals, those living in Germany and those who have returned to Germany.

Application of a hybrid tracking algorithm to motion analysis

An algorithm for tracking multiple feature positions in a dynamic image sequence is presented. This is achieved using a combination of two trajectory-based methods, with the resulting hybrid algorithm exhibiting the advantages of both. An optimizing exchange algorithm is described which enables short feature paths to be tracked without prior knowledge of the motion being studied. The resulting partial trajectories are then used to initialize a fast predictor algorithm which is capable of rapidly tracking multiple feature paths. As this predictor algorithm becomes tuned to the feature positions being tracked, it is shown how the location of occluded or poorly detected features can be predicted. The results of applying this tracking algorithm to data obtained from real-world scenes are then presented.

Benchmark datasets for detection and tracking

There is a rising demand for the quantitative performance evaluation of automated video surveillance. To advance research in this area, it is essential that comparisons in detection and tracking approaches may be drawn and improvements in existing methods can be measured. There are a number of challenges related to the proper evaluation of motion segmentation, tracking, event recognition, and other components of a video surveillance system that are unique to the video surveillance community. These include the volume of data that must be evaluated, the difficulty in obtaining ground truth data, the definition of appropriate metrics, and achieving meaningful comparison of diverse systems. This chapter provides descriptions of useful benchmark datasets and their availability to the computer vision community. It outlines some ground truth and evaluation techniques, and provides links to useful resources. It concludes by discussing the future direction for benchmark datasets and their associated processes.

An eye for an eye: robust motion segmentation by applying the latest in human vision research

Automatically extracting interesting objects from videos is a very challenging task and is applicable to many research areas such robotics, medical imaging, content based indexing and visual surveillance. Automated visual surveillance is a major research area in computational vision and a commonly applied technique in an attempt to extract objects of interest is that of motion segmentation. Motion segmentation relies on the temporal changes that occur in video sequences to detect objects, but as a technique it presents many challenges that researchers have yet to surmount. Changes in real-time video sequences not only include interesting objects, environmental conditions such as wind, cloud cover, rain and snow may be present, in addition to rapid lighting changes, poor footage quality, moving shadows and reflections. The list provides only a sample of the challenges present. This thesis explores the use of motion segmentation as part of a computational vision system and provides solutions for a practical, generic approach with robust performance, using current neuro-biological, physiological and psychological research in primate vision as inspiration.

Mouse movements of motion-impaired users: a submovement analysis

Understanding human movement is key to improving input devices and interaction techniques. This paper presents a study of mouse movements of motion-impaired users, with an aim to gaining a better understanding of impaired movement. The cursor trajectories of six motion-impaired users and three able-bodied users are studied according to their submovement structure. Several aspects of the movement are studied, including the frequency and duration of pauses between submovements, verification times, the number of submovements, the peak speed of submovements and the accuracy of submovements in two-dimensions. Results include findings that some motion-impaired users pause more often and for longer than able-bodied users, require up to five times more submovements to complete the same task, and exhibit a correlation between error and peak submovement speed that does not exist for able-bodied users.

Gravity well visibility in haptic interfaces for motion-impaired

Haptic computer interfaces provide users with feedback through the sense of touch, thereby allowing users to feel a graphical user interface. Force feedback gravity wells, i.e. attractive basins that can pull the cursor toward a target, are one type of haptic effect that have been shown to provide improvements in "point and click" tasks. For motion-impaired users, gravity wells could improve times by as much as 50%. It has been reported that the presentation of information to multiple sensory modalities, e.g. haptics and vision, can provide performance benefits. However, previous studies investigating the use of force feedback gravity wells have generally not provided visual representations of the haptic effect. Where force fields extend beyond clickable targets, the addition of visual cues may affect performance. This paper investigates how the performance of motion-impaired computer users is affected by having visual representations of force feedback gravity wells presented on-screen. Results indicate that the visual representation does not affect times and errors in a "point and click" task involving multiple targets.

The effect of multiple haptic distractors on the performance of motion-impaired users

The effect of multiple haptic distractors on target selection performance was examined in terms of times to select the target and the associated cursor movement patterns. Two experiments examined: a) The effect of multiple haptic distractors around a single target and b) the effect of inter-item spacing in a linear selection task. It was found that certain target-distractor arrangements hindered performance and that this could be associated with specific, explanatory cursor patterns. In particular, it was found that the presence of distractors along the task axis in front of the target was detrimental to performance, and that there was evidence to suggest that this could sometimes be associated with consequent cursor oscillation between distractors adjacent to a desired target. A further experiment examined the effect of target-distractor spacing in two orientations on a user’s ability to select a target when caught in the gravity well of a distractor. Times for movements in the vertical direction were found to be faster than those in the horizontal direction. In addition, although times for the vertical direction appeared equivalent across five target-distractor distances, times for the horizontal direction exhibited peaks at certain distances. The implications of these results for the design and implementation of haptically enhanced interfaces using the force feedback mouse are discussed.