A Fast Biologically Inspired Algorithm for Recurrent Motion Estimation (Motion Detection)

ABSTRACT

Project Title:

A Fast Biologically Inspired Algorithm for Recurrent Motion Estimation (Motion Detection)

Journal: IEEE Computer Society ISSN:0162-8828

Introduction:

          A neurodynamical model of motion segregation in cortical visual area V1 and MT of the dorsal stream. The model explains how motion ambiguities caused by the motion aperture problem can be solved for coherently moving objects of arbitrary size by means of cortical mechanisms.

The major bottleneck in the development of a reliable biologically inspired technical system with real-time motion analysis capabilities based on this neural model is the amount of memory necessary for the representation of neural activation in velocity space. Sparse coding frameworks for neural motion activity patterns and suggest a means by which initial activities are detected efficiently.

A neural mechanism such as shunting inhibition and feedback modulation in the sparse framework to implement an efficient algorithmic version of our neural model of cortical motion segregation. We demonstrate that the algorithm behaves similarly to the original neural model and is able to extract image motion from real world image sequences.

A neuroscience model of cortical motion computation to achieve technologically demanding constraints such as real-time performance and hardware implementation. In addition, the proposed biologically inspired algorithm provides a tool for modeling investigations to achieve acceptable simulation time

Index Terms:

          Motion estimation, computational models of vision, recurrent information processing, motion aperture problem, algorithms.

ENVIRONMENT:

Servers:

            Operating System Server: Windows XP or later

            Tools: Microsoft Visual Studio .Net-2005 (2.0)

            Code Behind: VC#.Net

Hardware Specification:

Processor: Intel Pentium or More

RAM: 1 GB Ram

Hard Disk: PC with 20GB

Device: Web Cam