Detection of Moving Objects Using Self-motion Constraints on Optic Flow

TL;DR

This study investigates how humans detect moving objects by exploiting self-motion constraints on retinal velocities, demonstrating that deviations from these constraints enable motion detection in virtual reality environments.

Contribution

The paper introduces a novel hypothesis that humans use self-motion constraints on optic flow to identify moving objects, supported by VR experiments showing reliance on velocity deviations.

Findings

Performance depends on deviation from constraint segments.

Detection accuracy varies with depth information precision.

End points of constraints reflect depth perception quality.

Abstract

As we move through the world, the pattern of light projected on our eyes is complex and dynamic, yet we are still able to distinguish between moving and stationary objects. We propose that humans accomplish this by exploiting constraints that self-motion imposes on retinal velocities. When an eye translates and rotates in a stationary 3D scene, the velocity at each retinal location is constrained to a line segment in the 2D space of retinal velocities. The slope and intercept of this segment is determined by the eye's translation and rotation, and the position along the segment is determined by the local scene depth. Since all possible velocities arising from a stationary scene must lie on this segment, velocities that are not must correspond to objects moving within the scene. We hypothesize that humans make use of these constraints by using deviations of local velocity from these constraint lines to detect moving objects. To test this, we used a virtual reality headset to present rich wide-field stimuli, simulating the visual experience of translating forward in several virtual environments with varied precision of depth information. Participants had to determine if a cued object moved relative to the scene. Consistent with the hypothesis, we found that performance depended on the deviation of the object velocity from the constraint segment, rather than a difference between retinal velocities of the object and its local surround. We also found that the endpoints of the constraint segment reflected the precision of depth information available in the different virtual environments.