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Research: Human Visual Navigation
Associate Professor John A. Perrone.
Modelling aspects of human self-motion estimation.
Some of
this research was conducted in collaboration with Dr. Leland S. Stone of the
Flight Management and Human Factors Division, NASA Ames Research Center, Moffett
Field, CA. U.S.A. (This page last edited on 2nd February
2007).
The successful manoeuvring of a person or vehicle through a cluttered environment requires information about
possible obstacles (the layout) as well as the instantaneous motion of the observer and craft (heading
direction and rotation). The latter 'self-motion' or egomotion information is required to help decide if any
corrective motor inputs are needed for collision avoidance or for a change in the desired direction of travel.
This navigational ability underlies many aspects of human behavior (walking, running, driving, flying) as well
as many machine-based applications (autonomous vehicles, robotics).
The inputs, providing the self-motion feedback for these navigational skills, are many and varied.
They can be visual, vestibular, proprioceptive, motor-corollary or cognitive although vision appears to
dominate (see Henn, Cohen & Young, 1980 for a review). The visual component of self-motion perception was
first investigated by Gibson (1950, 1966) and has since received much attention (see Heeger & Jepson, 1992;
Warren, Morris & Kalish, 1988 for reviews). Efforts have mainly concentrated on the two-dimensional visual
motion cues (the retinal flow field) that can be used to extract self-motion and environmental layout
information. Psychophysical experiments have demonstrated that heading information, at least, can be
extracted purely from visual motion inputs (see Warren & Hannon, 1990; Stone & Perrone, 1997) although the
role of eye-movements still remains an issue (Royden, Banks & Crowell, 1992).
We developed a template model that was able to account for many of the physiological and psychophysical
aspects of visual self-motion estimation (Perrone, 1992, Perrone & Stone,
1994, 1998). It uses networks of direction- and speed-tuned input sensors similar to neurons in area MT of
primate visual cortex, to form detectors tuned to particular heading and rotation combinations.
The approach relies on speed and direction tuning at the level of the MT neurons rather than direct readouts of
the image velocity vectors (Perrone, 2001). The resulting detectors have similar response properties to
neurons found in area MST, the putative processing area for self-motion estimation (e.g., Kawano et al, 1984;
Saito et al., 1986; Duffy & Wurtz, 1991, 1996) We have successfully used the template model to emulate many of
the receptive field properties of MST neurons (Perrone & Stone,
1998).
The model has now been extended to deal with actual two-dimensional input sequences, rather than theoretical
vector flow fields. A model of two-dimensional motion sensors with properties similar to those found in area
MT has been developed and used as a front-end to the template model (see 2-d motion page). We are now at the stage where heading and scene layout can be
extracted from two-dimensional image sequences involving combined translation and rotation of the observer.
References.
Duffy, C. J. & Wurtz, R. H. (1991). Sensitivity of MST Neurons to Optic Flow Stimuli. I. A continuum of
response selectivity to large-field stimuli. Journal of Neurophysiology, 65, 1329-1345.
Duffy, C. J. & Wurtz, R. H. (1995). Response of monkey MST neurons to optic flow stimuli with shifted centers
of motion. Journal of Neuroscience, 15, 5192-5208.
Gibson, J. J. The perception of the visual world. Boston: Houghton Mifflin, 1950.
Gibson, J. J. The senses considered as perceptual systems. Boston: Houghton Mifflin, 1966.
Heeger, D. J. & Jepson, A. D. (1992). Subspace methods for recovering rigid motion I: Algorithm and
implementation. International Journal of Computer Vision,
Henn, V., Cohen, B. & Young, L. R. (1980). Visual-vestibular interaction in motion perception and the
generation of nystagmus. Neurosciences Research Program Bulletin, 18, 556-567.
Kawano, K., Sasaki, M. & Yamashita, M. (1984). Response properties of neurons in posterior parietal cortex of
monkey during visual-vestibular stimulation. I. Visual tracking neurons. Journal of Neurophysiology., 51,
340-351.
Perrone, J. A. (1992). Model for the computation of self-motion in biological systems. Journal of the
Optical Society of America, A., 9, 177-194.
Perrone, J. A. (1997). Extracting observer heading and scene layout from image sequences. Investigative
Ophthalmology and Visual Science, 38, S481.
Perrone, J.A. & Stone, L.S. (1994) A model of self-motion estimation within primate extrastriate visual
cortex. Vision Research . 34, 2917-2938
Perrone, J. A. & Stone, L.S. (1998) Emulating the visual receptive field properties of MST neurons with a
template model of heading estimation.
J.Neuroscience, 18, 5958-5975.
Perrone, J. A. (2001). A closer look at the visual input to
self-motion estimation. In J. M. Zanker & J. Zeil (Eds.), Motion
Vision. Computational, Neural, and Ecological Constraints (pp. 169-179).
Heidelberg: Springer-Verlag.
Royden, C. S., Banks, M. S. & Crowell, J. A. (1992). The perception of heading during eye movements.
Nature, 360, 583-585.
Saito, H., Yukie, M., Tanaka, K., Hikosaka, K., Fukada, Y. & Iwai, E. (1986). Integration of direction
signals of image motion in the superior temporal sulcus of the Macaque monkey. Journal of Neuroscience, 6,
145-157.
Stone, L. S. & Perrone, J. A. (1997). Human heading perception during visually simulated curvilinear motion.
Vision Research, 37, 573-590.
Warren, W. H., Morris, M. W. & Kalish, M. (1988). Perception of Translational Heading From Optical Flow.
Journal of Experimental Psychology. Human Perception and Performance., 14, 646-660.
Warren, W. H. & Hannon, D. J. (1988). Direction of self-motion is perceived from optical flow. Nature, 336,
162-163.
Warren, W. H. & Hannon, D. J. (1990). Eye movements and optical flow. Journal of the Optical Society of
America, A 7, 160-168.
Publications Relevant to Template Model:
Perrone J.A. (1987). Extracting 3-D egomotion information from a 2-D flow field: A biological solution?
Optical Society of America Technical Digest Series 22: 47. 63-74.
Perrone J.A. (1989). The perception of surface layout during low levelflight. (NASA CP 3118) Washington, DC:
National Aeronautics and Space Administration. 63-74.
Perrone J.A. (1989). In search of the elusive flow field. Workshop on Visual Motion. IEEE Computer Society
Press. 181-188.
Perrone J.A. (1990) Simple technique for optical flow estimation. Journal of the Optical Society of America A.
7, 264-278.
Perrone J.A. (1992) Model for the computation of self-motion in biological systems. Journal of the Optical
Society of America A, 9, 177-194.
Perrone J.A. & Stone, L.S. (1994) A model of self-motion estimation
within primate extrastriate visual cortex. Vision Research 34, 2917-2938.
Perrone J. A.(1994) Simulating the speed and direction tuning of MT neurons using spatiotemporal tuned
V1-neuron inputs. Investigative Ophthalmology and Visual Science, 38, S481.
Perrone, J. A. (1997). Extracting observer heading and scene layout from image sequences. Investigative
Ophthalmology and Visual Science, 35, 2158.
Perrone, J. A. & Stone, L.S. (1998) Emulating the visual receptive field properties of MST neurons with a
template model of heading estimation.
J.Neuroscience, 18, 5958-5975.
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