Moving from spatially segregated to transparent motion: A modelling approach

The combined effect of eye movements improve head centred local motion information during walking

Eye movements play multiple roles in human behaviour—small stabilizing movements are important for keeping the image of the scene steady during locomotion, whilst large scanning movements search for relevant information. It has been proposed that eye movement induced retinal motion interferes with the estimation of self-motion based on optic flow. We investigated the effect of eye movements on retinal motion information during walking. Observers walked towards a target, wearing eye tracking glasses that simultaneously recorded the scene ahead and tracked the movements of both eyes. By realigning the frames of the recording from the scene ahead, relative to the centre of gaze, we could mimic the input received by the retina (retinocentric coordinates) and compare this to the input received by the scene camera (head centred coordinates). We asked which of these coordinate frames resulted in the least noisy motion information. Motion noise was calculated by finding the error in between the optic flow signal and a noise-free motion expansion pattern. We found that eye movements improved the optic flow information available, even when large diversions away from target were made.

A bio-inspired, motion-based analysis of crowd behavior attributes relevance to motion transparency, velocity gradients, and motion patterns

The analysis of motion crowds is concerned with the detection of potential hazards for individuals of the crowd. Existing methods analyze the statistics of pixel motion to classify non-dangerous or dangerous behavior, to detect outlier motions, or to estimate the mean throughput of people for an image region. We suggest a biologically inspired model for the analysis of motion crowds that extracts motion features indicative for potential dangers in crowd behavior. Our model consists of stages for motion detection, integration, and pattern detection that model functions of the primate primary visual cortex area (V1), the middle temporal area (MT), and the medial superior temporal area (MST), respectively. This model allows for the processing of motion transparency, the appearance of multiple motions in the same visual region, in addition to processing opaque motion. We suggest that motion transparency helps to identify "danger zones" in motion crowds. For instance, motion transparency occurs in small exit passages during evacuation. However, motion transparency occurs also for non-dangerous crowd behavior when people move in opposite directions organized into separate lanes. Our analysis suggests: The combination of motion transparency and a slow motion speed can be used for labeling of candidate regions that contain dangerous behavior. In addition, locally detected decelerations or negative speed gradients of motions are a precursor of danger in crowd behavior as are globally detected motion patterns that show a contraction toward a single point. In sum, motion transparency, image speeds, motion patterns, and speed gradients extracted from visual motion in videos are important features to describe the behavioral state of a motion crowd.

Variation in the local motion statistics of real-life optic flow scenes

Optic flow motion patterns can be a rich source of information about our own movement and about the structure of the environment we are moving in. We investigate the information available to the brain under real operating conditions by analyzing video sequences generated by physically moving a camera through various typical human environments. We consider to what extent the motion signal maps generated by a biologically plausible, two-dimensional array of correlation-based motion detectors (2DMD) not only depend on egomotion, but also reflect the spatial setup of such environments. We analyzed the local motion outputs by extracting the relative amounts of detected directions and comparing the spatial distribution of the motion signals to that of idealized optic flow. Using a simple template matching estimation technique, we are able to extract the focus of expansion and find relatively small errors that are distributed in characteristic patterns in different scenes. This shows that all types of scenes provide suitable motion information for extracting ego motion despite the substantial levels of noise affecting the motion signal distributions, attributed to the sparse nature of optic flow and the presence of camera jitter. However, there are large differences in the shape of the direction distributions between different types of scenes; in particular, man-made office scenes are heavily dominated by directions in the cardinal axes, which is much less apparent in outdoor forest scenes. Further examination of motion magnitudes at different scales and the location of motion information in a scene revealed different patterns across different scene categories. This suggests that self-motion patterns are not only relevant for deducing heading direction and speed but also provide a rich information source for scene structure and could be important for the rapid formation of the gist of a scene under normal human locomotion.

A model of motion transparency processing with local center-surround interactions and feedback

Motion transparency occurs when multiple coherent motions are perceived in one spatial location. Imagine, for instance, looking out of the window of a bus on a bright day, where the world outside the window is passing by and movements of passengers inside the bus are reflected in the window. The overlay of both motions at the window leads to motion transparency, which is challenging to process. Noisy and ambiguous motion signals can be reduced using a competition mechanism for all encoded motions in one spatial location. Such a competition, however, leads to the suppression of multiple peak responses that encode different motions, as only the strongest response tends to survive. As a solution, we suggest a local center-surround competition for population-encoded motion directions and speeds. Similar motions are supported, and dissimilar ones are separated, by representing them as multiple activations, which occurs in the case of motion transparency. Psychophysical findings, such as motion attraction and repulsion for motion transparency displays, can be explained by this local competition. Besides this local competition mechanism, we show that feedback signals improve the processing of motion transparency. A discrimination task for transparent versus opaque motion is simulated, where motion transparency is generated by superimposing large field motion patterns of either varying size or varying coherence of motion. The model's perceptual thresholds with and without feedback are calculated. We demonstrate that initially weak peak responses can be enhanced and stabilized through modulatory feedback signals from higher stages of processing.

A model of neural mechanisms in monocular transparent motion perception

Transparent motion is perceived when multiple motions are presented in the same part of visual space that move in different directions or with different speeds. Several psychophysical as well as physiological experiments have studied the conditions under which motion transparency occurs. Few computational mechanisms have been proposed that allow to segregate multiple motions. We present a novel neural model which investigates the necessary mechanisms underlying initial motion detection, the required representations for velocity coding, and the integration and segregation of motion stimuli to account for the perception of transparent motion. The model extends a previously developed architecture for neural computations along the dorsal pathway, particularly, in cortical areas V1, MT, and MSTd. It emphasizes the role of feedforward cascade processing and feedback from higher to earlier processing stages for selective feature enhancement and tuning. Our results demonstrate that the model reproduces several key psychophysical findings in perceptual motion transparency using random dot stimuli. Moreover, the model is able to process transparent motion as well as opaque surface motion in real-world sequences of 3-d scenes. As a main thesis, we argue that the perception of transparent motion relies on the representation of multiple velocities at one spatial location; however, this feature is necessary but not sufficient to perceive transparency. It is suggested that the activations simultaneously representing multiple activities are subsequently integrated by separate mechanisms leading to the segregation of different overlapping segments.

Motion transparency from opposing luminance modulated and contrast modulated gratings

Two luminance gratings of identical orientation and opposite directions of motion are seen as moving across one another (i.e. moving transparently) only if they differ in spatial frequency (SF) by a factor of four or more. Identical SF gratings produce counter-phase flicker. This suggests that opposite motions cancel each other at the level of motion detection. Here we show that motion transparency is perceived with two gratings of the same SF and orientation moving in opposite directions, when one grating is a first-order, luminance modulated (LM) stimulus and the other is a second-order, contrast modulated (CM) stimulus. Participants were presented with various combinations of LM and CM gratings. In experiment 1, the test stimulus contained the summation of oppositely moving LM and CM gratings. In order to assess the simultaneous perception of both motions, we used a paradigm where observers were required to discriminate the direction of motion of each component from counter-phase flicker. Results show that observers can accurately discriminate both LM and CM directions of motion in a transparent configuration. We next measured the effect of varying the contrast/modulation depth of LM and CM gratings on the perception of transparency. The perception of motion transparency depends upon the relative contrast/modulation depth of the component gratings: raising the contrast of the LM component necessitates a greater modulation depth for the CM component if motion transparency is to be perceived. Our results are consistent with a motion system comprised of two separate, but not wholly independent, pathways for the encoding of LM and CM signals. We hypothesise that the observed contrast dependence is the result of contrast gain control mechanisms that receive inputs from separate motion systems.

An oblique effect for transparent-motion detection caused by variation in global-motion direction-tuning bandwidths

Despite evidence for the broad direction tuning of global-motion detectors, transparent motion can be detected with comparatively small angular separations. The exact means by which this broad population response is decoded to yield multiple signal directions remains unclear. Consequently, we sought to determine the relationship between angular separation thresholds for transparent motion and the direction-tuning bandwidth of global-motion detectors. Angular separation thresholds were assessed around four axes of motion, with thresholds lower around cardinal axes than the oblique axes. This was also found with lowered signal intensities, despite larger differences between the component directions at threshold, indicating that the transparency oblique effect relies more on the mean direction than the components. Simulations with a model global-motion population suggest this is likely to arise from variation in direction-tuning bandwidths around the cardinal and oblique axes. In a second experiment, adaptation to oblique unidirectional motion produced threshold elevation for a wider range of test directions than adaptation to a cardinal direction. This is consistent with tighter direction tuning around cardinal axes and provides a basis for the transparent-motion oblique effect. Our narrow bandwidth estimates also suggest that transparent-motion detection could rely on bimodal activity within the global-motion stage.