Motion Detection Using MATLAB

1. Motion Detection Using MATLAB Laura DeMar Jin Han Advisor: Professor Rudko

2. Project Goals • Model an animal’s visual perception of movements in natural scenes • Use an elementary motion detector of the correlation type • Study an animal’s response to an idealized input stimulus • Provide a correct model that gives insight into insect’s reaction to prey and food and non-reaction to background motion

3. Introduction • Bernhard Hassentein and Werner Reichardt • Did series of behavioral experiments on motion vision in insects • Wasps, bees, and flies • Led to development of the correlation-type motion detector, also known as the Reichardt Detector

4. Correlation Model • Image Frames • Object moves by n0 pixels from frame to frame • Imprint • Delayed by a time constant D • Output • Multiplication of this object with its imprint • Maximum response when D = n0 • Two mirror subunits • Each subunit has a delay and multiplication

5. Spatial filter • Symmetric Gaussian filter • Edge enhancement • Spatially filtered object with size L=20. n0 = 1, D = 6, and tau = 1.5 • Spatial Filter mask

6. Temporal Filter • First-order lowpass filter • Filtered response decays exponentially • Filter gain is inversely proportional to the time constant • Spatially and Temporally Filtered Object with length L=6. Time constant is 1.5 and D=3

7. Object Simulation • Idealized object • Matrix of ones representing a white rectangle • Size: length L, width K (Pixels) • Moves across the screen in frames • Variable size and direction • Constant velocity

8. Filtered Moving Object • Temporally filtered signals mimic those that would appear in lizard’s retinal image. They decay with time constant tau • The larger tau, the longer the memory of the filter • Temporally filtered object • for tau=20 and n0 = 2

9. Results • Calculated the size of the maximum response for various object sizes, speeds, and memory time constants • For small objects, the gain of the temporal filter is inversely proportional to tau. • The maximum response peaks when the velocity is equal to the correlator distance

10. The Effect of the Time Constant For Different Object Sizes • There is a shift in the peak of the maximum response for large object sizes with different tau. • The peak of the maximum response does not occur when the velocity is equal to the correlation distance (D = 6). • Large objects are not tuned for different values of tau.

11. The Effect of Tau on Correlation Distance and Tuning (Large Object) • For small correlation distance (D) and small tau, the response is broader • The tuning of the motion detector depends on tau

12. Future Work • Acceleration • Swaying motion • Relation with physiological results

13. References • J.M Zanker, M.V. Srinivasan, M. Egelhaaf. “Speed Tuning in Elementary Motion Detectors Of the Correlation Type”. Biological Cybernetics, 1999. • Alexander Borst and Martin Egelhaaf. “Principles of Visual Motion Detection”. TINS, Vol. 12, No 8, 1989. • Alexander Borst. “Models of Motion Detection”. Nature neuroscience supplement, Vol. 3, November 2000. • Michael P. Eckert and Jochen Zeil. “Towards an Ecology of Motion Vision”. In:  Motion Vision - Computational, Neural, and Ecological Constraints (Edited by Johannes M. Zanker and Jochen Zeil) Springer Verlag, Berlin Heidelberg New York, 2001. • Johannes M. Zanker. “An Early-Vision Computational Model to Analyze Motion Signal Distributions in Artificial and Natural Stimuli”. Department of Psychology, Royal Holloway University of London, England. 2001

14. Questions?