Close coupling between eye movements and serial attentional refreshing during multiple-identity tracking

Multiple-target tracking in human and machine vision

Humans are able to track multiple objects at any given time in their daily activities—for example, we can drive a car while monitoring obstacles, pedestrians, and other vehicles. Several past studies have examined how humans track targets simultaneously and what underlying behavioral and neural mechanisms they use. At the same time, computer-vision researchers have proposed different algorithms to track multiple targets automatically. These algorithms are useful for video surveillance, team-sport analysis, video analysis, video summarization, and human–computer interaction. Although there are several efficient biologically inspired algorithms in artificial intelligence, the human multiple-target tracking (MTT) ability is rarely imitated in computer-vision algorithms. In this paper, we review MTT studies in neuroscience and biologically inspired MTT methods in computer vision and discuss the ways in which they can be seen as complementary.

Multiple-target tracking (MTT) is a challenging task vital for both a human’s daily life and for many artificial intelligent systems, such as those used for urban traffic control. Neuroscientists are interested in discovering the underlying neural mechanisms that successfully exploit cognitive resources, e.g., spatial attention or memory, during MTT. Computer-vision specialists aim to develop powerful MTT algorithms based on advanced models or data-driven computational methods. In this paper, we review MTT studies from both communities and discuss how findings from cognitive studies can inspire developers to construct higher performing MTT algorithms. Moreover, some directions have been proposed through which MTT algorithms could raise new questions in the cognitive science domain, and answering them can shed light on neural processes underlying MTT.

How selective attention affects the detection of motion changes with peripheral vision in MOT

In sports, peripheral vision is expected to play an important role in tasks that demand distributed attention and motion-change detection. By using the Multiple-Object-Tracking (MOT) task, these demands were simulated in a well-controlled laboratory environment. Participants tracked four target out of ten moving objects (6 distractors) and pressed a button when one of the ten objects stopped. Detection rates for tracked targets were compared to detection rates of non-tracked distractors at eccentricities between 5° and 25°. The study's aim was to test how the location of attention affects peripheral motion detection. The results show a large attention effect because target stops were detected in 89 % and distractor stops only in 55 % of the trials. Distractor stops were more likely detected when they occurred closer to the fovea while target stops were detected at all eccentricities. That means, orienting attention at target objects facilitates the peripheral detection of their motion changes in monitoring tasks. Having distractors closer to the fovea increases the chance to also detect motion changes of unattended objects. On a theoretical level, results support a tracking mechanism with object-based attention, serial covert attention shifts and flexible but limited attentional resources. On a practical level, sports' experts should use their extensive knowledge to locate attention on most-relevant objects and reduce the eccentricity to other objects to detect motion changes of attended and unattended objects.

Eye Behavior During Multiple Object Tracking and Multiple Identity Tracking

We review all published eye-tracking studies to date that have used eye movements to examine multiple object (MOT) or multiple identity tracking (MIT). In both tasks, observers dynamically track multiple moving objects. In MOT the objects are identical, whereas in MIT they have distinct identities. In MOT, observers prefer to fixate on blank space, which is often the center of gravity formed by the moving targets (centroid). In contrast, in MIT observers have a strong preference for the target-switching strategy, presumably to refresh and maintain identity-location bindings for the targets. To account for the qualitative differences between MOT and MIT, two mechanisms have been posited, a position tracking (MOT) and an identity tracking (MOT & MIT) mechanism. Eye-tracking studies of MOT have also demonstrated that observers execute rescue saccades toward targets in danger of becoming occluded or are about to change direction after a collision. Crowding attracts the eyes close to it in order to increase visual acuity for the crowded objects to prevent target loss. It is suggested that future studies should concentrate more on MIT, as MIT more closely resembles tracking in the real world.

Model of Multiple Identity Tracking (MOMIT) 2.0: Resolving the serial vs. parallel controversy in tracking

The present study investigated whether during tracking of multiple moving objects with distinct identities only one identity is tracked at each moment (serial tracking) or whether multiple identities can be tracked simultaneously (parallel tracking). By adopting the gaze-contingent display change technique, we manipulated in real time the presence/absence of object identities during tracking. The data on performance accuracy revealed a serial tracking pattern for facial images and a parallel pattern for color discs: when tracking faces, the presence/absence of only the currently foveated identity impacted the performance, whereas when tracking colors, the presence of multiple identities across the visual field led to improved tracking performance. This pattern is consistent with the identifiability of the different types of objects in the visual field. The eye movements during MIT showed a bias towards visiting and dwelling on individual targets when facial identities were present and towards visiting the blank areas between targets when color identities were present. Nevertheless, the eye visits were predominately on individual targets regardless of the type of objects and the presence of object identities. The eye visits to targets were beneficial for target tracking, particularly in face tracking. We propose the Model of Multiple Identity Tracking (MOMIT) 2.0 which accounts for the results and reconcile the serial vs. parallel controversy. The model suggests that observers cooperatively use attention, eye movements, perception, and working memory for dynamic tracking. Tracking appears more serial when high-resolution information needs to be sampled and maintained for discriminating the targets, whereas it appears more parallel when low-resolution information is sufficient.