Independent Motion Detection with Event-driven Cameras

Unlike standard cameras that send intensity images at a constant frame rate, event-driven cameras asynchronously report pixel-level brightness changes, offering low latency and high temporal resolution (both in the order of micro-seconds). As such, they have great potential for fast and low power vision algorithms for robots. Visual tracking, for example, is easily achieved even for very fast stimuli, as only moving objects cause brightness changes. However, cameras mounted on a moving robot are typically non-stationary and the same tracking problem becomes confounded by background clutter events due to the robot ego-motion. In this paper, we propose a method for segmenting the motion of an independently moving object for event-driven cameras. Our method detects and tracks corners in the event stream and learns the statistics of their motion as a function of the robot's joint velocities when no independently moving objects are present. During robot operation, independently moving objects are identified by discrepancies between the predicted corner velocities from ego-motion and the measured corner velocities. We validate the algorithm on data collected from the neuromorphic iCub robot. We achieve a precision of ~ 90 % and show that the method is robust to changes in speed of both the head and the target.Comment: 7 pages, 6 figure

On the factors causing processing difficulty of multiple-scene displays

Multiplex viewing of static or dynamic scenes is an increasing feature of screen media. Most existing multiplex experiments have examined detection across increasing scene numbers, but currently no systematic evaluation of the factors that might produce difficulty in processing multiplexes exists. Across five experiments we provide such an evaluation. Experiment 1 characterises difficulty in change detection when the number of scenes is increased. Experiment 2 reveals that the increased difficulty across multiple-scene displays is caused by the total amount of visual information accounts for differences in change detection times, regardless of whether this information is presented across multiple scenes, or contained in one scene. Experiment 3 shows that whether quadrants of a display were drawn from the same, or different scenes did not affect change detection performance. Experiment 4 demonstrates that knowing which scene the change will occur in means participants can perform at monoplex level. Finally, Experiment 5 finds that changes of central interest in multiplexed scenes are detected far easier than marginal interest changes to such an extent that a centrally interesting object removal in nine screens is detected more rapidly than a marginally interesting object removal in four screens. Processing multiple-screen displays therefore seems dependent on the amount of information, and the importance of that information to the task, rather than simply the number of scenes in the display. We discuss the theoretical and applied implications of these findings

See no Evil: Challenges of security surveillance and monitoring

While the development of intelligent technologies in security surveillance can augment human capabilities, they do not replace the role of the operator entirely; as such, when developing surveillance support it is critical that limitations to the cognitive system are taken into account. The current article reviews the cognitive challenges associated with the task of a CCTV operator: visual search and cognitive/perceptual overload, attentional failures, vulnerability to distraction, and decision-making in a dynamically evolving environment. While not directly applied to surveillance issues, we suggest that the NSEEV (noticing – salience, effort, expectancy, value) model of attention could provide a useful theoretical basis for understanding the challenges faced in detection and monitoring tasks. Having identified cognitive limitations of the human operator, this review sets out a research agenda for further understanding the cognitive functioning related to surveillance, and highlights the need to consider the human element at the design stage when developing technological solutions to security surveillance

Top-down and bottom-up aspects of active search in a real-world environment

Visual search has been studied intensively in the labouratory, but lab search often differs from search in the real world in many respects. Here, we used a mobile eye tracker to record the gaze of participants engaged in a realistic, active search task. Participants were asked to walk into a mailroom and locate a target mailbox among many similar mailboxes. This procedure allowed control of bottom-up cues (by making the target mailbox more salient; Experiment 1) and top-down instructions (by informing participants about the cue; Experiment 2). The bottom-up salience of the target had no effect on the overall time taken to search for the target, although the salient target was more likely to be fixated and found once it was within the central visual field. Top-down knowledge of target appearance had a larger effect, reducing the need for multiple head and body movements, and meaning that the target was fixated earlier and from further away. Although there remains much to be discovered in complex real-world search, this study demonstrates that principles from visual search in the labouratory influence gaze in natural behaviour, and provides a bridge between these labouratory studies and research examining vision in natural tasks. (PsycINFO Database Record © 2014 APA, all rights reserved)

Event-Based Motion Segmentation by Motion Compensation

In contrast to traditional cameras, whose pixels have a common exposure time, event-based cameras are novel bio-inspired sensors whose pixels work independently and asynchronously output intensity changes (called "events"), with microsecond resolution. Since events are caused by the apparent motion of objects, event-based cameras sample visual information based on the scene dynamics and are, therefore, a more natural fit than traditional cameras to acquire motion, especially at high speeds, where traditional cameras suffer from motion blur. However, distinguishing between events caused by different moving objects and by the camera's ego-motion is a challenging task. We present the first per-event segmentation method for splitting a scene into independently moving objects. Our method jointly estimates the event-object associations (i.e., segmentation) and the motion parameters of the objects (or the background) by maximization of an objective function, which builds upon recent results on event-based motion-compensation. We provide a thorough evaluation of our method on a public dataset, outperforming the state-of-the-art by as much as 10%. We also show the first quantitative evaluation of a segmentation algorithm for event cameras, yielding around 90% accuracy at 4 pixels relative displacement.Comment: When viewed in Acrobat Reader, several of the figures animate. Video: https://youtu.be/0q6ap_OSBA

Augmented Reality HUDs: Warning Signs and Drivers’ Situation Awareness

Drivers must search dynamic and complex visual environments to perceive relevant environmental elements such as warning signs, pedestrians and other vehicles to select the appropriate driving maneuver. The objective of this research was to examine how an Augmented Reality Head Up Display (AR HUD) for warning signs affects driver Situation Awareness (SA) and attention. Participants viewed videos of real driving scenes with an AR HUDs or no display and were asked to report what elements in the driving scene attracted their attention. At the completion of the first driving video participants were given a warning sign recognition test. Participants then watched a second video and the Situation Awareness Global Assessment Technique (SAGAT), a measure of global SA was administered. Participants eye movements were recorded when watching the videos to investigate how drivers interacting with an AR HUD attend to the environment compared to drivers with no AR HUD. AR HUDs for warning signs are effective in making warning signs more attentionally conspicuous to drivers in both low and high clutter driving environments. The HUD did not lead to increased fixation duration or frequency to warning signs in many situations. However when two driving items were in sight (sign and car) and participants needed to decide where to attend, they experienced attentional tunneling. In complex driving situations participants spent a significantly longer proportion of time looking at warning signs in the HUD. In simple driving situations, AR HUDs appear to make warning signs more salient and conspicuous. However, in complex situations in high clutter driving environments AR HUDs may lead to attentional tunneling

Change blindness: eradication of gestalt strategies

Arrays of eight, texture-defined rectangles were used as stimuli in a one-shot change blindness (CB) task where there was a 50% chance that one rectangle would change orientation between two successive presentations separated by an interval. CB was eliminated by cueing the target rectangle in the first stimulus, reduced by cueing in the interval and unaffected by cueing in the second presentation. This supports the idea that a representation was formed that persisted through the interval before being 'overwritten' by the second presentation (Landman et al, 2003 Vision Research 43149–164]. Another possibility is that participants used some kind of grouping or Gestalt strategy. To test this we changed the spatial position of the rectangles in the second presentation by shifting them along imaginary spokes (by ±1 degree) emanating from the central fixation point. There was no significant difference seen in performance between this and the standard task [F(1,4)=2.565, p=0.185]. This may suggest two things: (i) Gestalt grouping is not used as a strategy in these tasks, and (ii) it gives further weight to the argument that objects may be stored and retrieved from a pre-attentional store during this task