bio-inspired vision system

8/8/2019 Bio-Inspired Vision System

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Bio-Inspired Vision System

ForAutomobiles

Submitted bySobin Chandras Jayaram

(7946)

Guide:Mrs. Sheeba.O


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Bio-Inspired Vision System ForAutomobiles

Introduction

A smart car should primarily be able of sensing itsstate.

Making cars safer is one major challenges of theautomotive industry.

Advanced driver assistance systems are build intocommercial cars to assist the driver and preventaccidents from happening.

A large part of these systems is vision based.


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ADAS an Insight

Advanced Driver Assistance Systems areembedded computing systems whichperforms actions which can assist the

driver e.g. Lane Detection

Driver Drowsiness Detection

Sign Board Recognition

Collision Detection

Night Vision


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All about ADAS


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ADAS : Lacking Something?

Development of vision for automobiles isslowed down by the challenges in analysisof rapidly changing image flows in real-

time.

Huge amount of data images contain.

Large computational and memoryresources needed for real-time completionof even conceptually simple task.


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Do you think we are Lacking an highend reference system to compare

with?

Definitely

Where Man fails Mother Nature Wins.

Natural Vision systems havebeen improved throughmillennia of evolution and aremore robust, compact, andefficient than artificialcounterparts.


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Why Biological Models?

Vision systems improved throughmillennia of evolution.

Insects rely on vision for cruise control,navigation and collision avoidance.

Why not to take advantage of theknowledge about these systems?


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Underlying Biological Models

Lobula GiantMovement Detector(LGMD)

Topological FeatureEstimator (TpFE)

Attention FocusingA

lgorithm (A

FA

)


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LGMD Behavior and Emulation

Basic components of the proposed bio-inspiredsystem contain input visual information.

Input Visual information is passed to the LGMDmodule for evaluation.

Spatial attention of that LGMD is driven by the AFAmodule.

Finally, information regarding the nature of theinput object is extracted by the TpFE module.


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Computational LGMD Module

The model parameters and state variablesare tuned such that everything can besatisfactorily executed.

The LGMD threshold is made adaptiveinstead of constant

Threat scenarios are classified accordingto the firing rate of a neuron whichreceives information from LGMD andThreshold Cell.


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Attention Focusing Algorithm

Heuristic search showsinformation is containedwithin reduced regions of thelast model layers

Focusing of region of interestreduces amount ofinformation to be processed.

The AFE divides the inputframe by defining anattention grid formed by thegroup of cells, some of whichare enabled other disabled.

Modules are used foractivation of attention cells.

Attention, Activating andDisabling Modules


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Topological Feature Estimator

LGMD potential calculationinvolves loss of informationabout shape of object, howeverit is vital in many cases.

Sharp turning or bulge in roadcan shake the camera makingthe background move rapidly.

TpFE module overcomes theseproblems by early classificationof approaching objects.

Vertical-shaped, Horizontal-shaped, Spatially Global,Square-shaped class.


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Augmented Features of

Intelligent Vehicles

Lane Departure Warning


Sign Board Recognition

Collision Detection and Warning


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Lane Departure Warning

To navigate autonomously or follow a road,

intelligent vehicles need to detect lanes.

It seems that the best cue for lane detection is touse the lane markings painted on roads

The video sensors are the best candidate for

finding lane markings.


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Parameters Considered in

Lane Detection


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Drowsiness slows reaction time, decreasesawareness, and impairs judgment, just like drugsor alcohol.


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Continued..

A camera will be monitoring drivers eye.

If situation arises the warning system will be activated,alerting the driver

The eye regions in the face present great intensitychanges, the eyes are located using intensity changes inthe face.

Once the eyes are located, measuring the distances

between the intensity changes in the eye areadetermine whether the eyes are open or closed.

If the eyes are found closed for 5 consecutive frames,the system issues a warning signal.


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Parametric Variation


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Sign-Board Recognition

The principle of Pattern Recognition is employed inSign-board classification.

Traffic signals and Sign boards are employed so as

to direct the driver.

A database of various signs to be recognised is

made.

The real-time image is compared with the

averaged image present in the database

Decision are made with image having highestcorrelation


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Components of Pattern

Recognition System

A Sensor

A Pre-processing mechanism

A Feature extraction mechanism

A Classification Algorithm A Set of examples already classified or described


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Thrust Areas of Research

Adaptive Cruise Control

Lane Keeping Assistance

Pre-Crash Safety

Blind spot Detection/Lane ChangeAssistant

Night Vision

Motion Stabilization

Parking Assistant


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Benefits vs. Disadvantages

More safety and comfort for the driver and other

traffic participants.

Better utilization of the infrastructure, lowerenergy consumption.

Users of advanced driver assistance systems havethe tendency to perform other activities, because

they have a false sense of security.

The driver can intervene when the system makes amistake, but also a disadvantage, because the

driver can make a wrong intervention.


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Conclusion

Conventional engineering methodologies follow atop-down approach. Alternatively, biology hasevolved to solve complex sensory-perception-actuation problems by following what can be

considered as a bottom-up approach.

Future systems will be integrated with othersystems and these future systems will requiremore computational power.

More Research is needed to develop an practicallyfail-safe assistance system.


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References

[1] Gustavo Linan-Cembrano, Luis Carranza, Clair Rind,Akos Zarandy, Martti Soininen, and Angel Rodriguez-Vasquez, Insect-Vision Inspired Collision Warning VisionProcessor for Automobiles, IEEE Circuits and Systems, Vol.8, pp. 6-24, 2008

[2]R.Bishop , A survey of intelligent vehicle applicationsworldwide. Proc. of the IEEE 2000 Intelligent Symposium,2006, pp. 25-30.

[3]M.Bertozzi, et al., Artificial vision in road vehicles,Proceedings of the IEEE, vol. 90, July 2006, pp. 1258-1271.


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bio-inspired vision system

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