# Flow parsing as causal source separation allows fast and parallel object and self-motion estimation

## TL;DR

This paper introduces a computational model that mimics human ability to separate self-motion and object-motion from optic flow using flow parsing.

## Contribution

The novel contribution is a model implementing flow parsing via heading likelihood maps to estimate self-motion and object motion in parallel.

## Key findings

- The model accurately estimates heading and object motion from optic flow.
- Simulations show the model replicates human performance in varying object motion conditions.
- Heading estimation depends on object speed and direction, mirroring human behavior.

## Abstract

Optic flow, the retinal pattern of motion experienced during self-motion, contains information about one’s direction of heading. The global pattern due to self-motion is locally confounded when moving objects are present, and the flow is the sum of components due to the different causal sources. Nonetheless, humans can accurately retrieve information from such flow, including the direction of heading and the scene-relative motion of an object. Flow parsing is a process speculated to allow the brain’s sensitivity to optic flow to separate the causal sources of retinal motion in information due to self-motion and information due to object motion. In a computational model that retrieves object and self-motion information from optic flow, we implemented flow parsing based on heading likelihood maps, whose distributions indicate the consistency of parts of the flow with self-motion. This allows for concurrent estimation of heading, detecting and localizing a moving object, and estimating its scene-relative motion. We developed a paradigm that allows the model to perform all these estimations while systematically varying the object’s contribution to the flow field. Simulations of that paradigm show that the model replicates many aspects of human performance, including the dependence of heading estimation on object speed and direction.

A model using flow parsing based on heading likelihood maps extracts information about self- and object-motion from optic flow, mirroring human performance across conditions with varying object motion.