REAL TIME IMAGE PROCESSING

From research to reality

GUIDE:SHEENA S

By:

SONIYA KUMARI

OVERVIEW INTRODUCTION

HARDWARE PLATFORM TO RTIP

RTIP ON DISTRIBUTED COMPUTER SYSTEMS

RTIP APPLIED TO TRAFFIC QUEUE

APPLICATIONS

CONCLUSION

INTRODUCTION

Image Processing

Real Time

Real-time in the perceptual sense

Real-time in the software engineering sense. Real-time in the signal processing

sense.

REAL TIME IMAGE PROCESSING

What is Real-time Image Processing?

How it differs from ordinary Image Processing?

What is the need for RTIP?

REAL-TIME IMAGE PROCESSING

System Design

Haredware Selection and Software Performance both are crucial.

camera ADC driver RTIP displaybus

software

REAL-TIME IMAGE PROCESSING

Platform Requirements: • high resolution, high frame rate video input • low latency video input

• low latency operating system scheduling

• high processing performance

SAMPLING RESOLUTION

What is the need for Sampling Resolution?

Spatial resolution and temporal resolution are both crucial

camera ADC driver RTIP displaybus

LOW LATENCY VIDEO INPUT

Latency targets

perceived synchronicity

Unavoidable latency1 to 2 frames(40 - 80ms for PAL)

Additional latency must be minimized

LOW LATENCY OPERATING SYSTEM SCHEDULING

Processing of video signals depend on -video capture hardware in use. -driver component.

Software components has crucial impact on system latency.

To avoid loss of input data, buffering is introduced to cover lag.

Mac OS X has excellent low latency performance.

HIGH PROCESSING PERFORMANCE

Both latency and throughput are important

PAL video frame: 884Kb Sustained data rate:22Mb/s

Memory bandwidth is crucial.

AltiVec is very useful .

MAC OS X Mac OS X is the world’s most advanced operating

system.

Features:

Power of unix simplicity of MAC. Perfect integration of hardware and software. Elegant interface and stunning graphics. Highly secure by design. Innovation for everyone. Reliable to the core.

SOFTWARE OPERATIONS INVOLVED IN RTIP

Levels of Image Processing:

LOW-LEVEL

INTERMEDIATE -LEVEL

HIGH -LEVEL

Low-level operations

Intermediate -level operations

High-level operations

LOW LEVEL OPERATIONS

Low-level operators take an image as their input and produce an image as their output.

It transform image data to image data i.e it deal directly with image matrix data at the pixel

level.

Examples:-color transformations, gamma correction, linear or nonlinear filtering, noise reduction etc.

Goal of Low Level Operation.

INTERMEDIATE LEVEL OPERATIONS

It transform image data to a slightly more abstract form of information by extracting certain attributes of image.

Ultimate goal is to reduce the amount of data to form a set of features suitable for further high-level processing.

Examples:-segmentation of image into regions/objects of interest, extracting edges etc.

HIGH LEVEL OPERATIONS

It interpret the abstract data from the intermediate-level, performing high level knowledge-based scene analysis on a reduced amount of data.

They are less data intensive and more inherently sequential rather than parallel.

RTIP APPLIED ON TRAFFIC-QUEUE DETECTION ALGORITHM

Why RTIP applied to traffic? -For reducing congestion problem

Need for processing of traffic data -Traffic control -Traffic management -Road safety -Development of transport policy.

Traffic measurable parameters -Traffic volumes & Speed -Inter-vehicle gaps & Vehicle classification

Image analysis system structure: -

backing

store

data bus

CCTVcamera

ADC

RAM64kbytes

16-Bit mini-computers

Printer

DAC

Monitor

Stages of image analysis:-

Image sensors used

ADC Conversion

Pre-processing

To cope with this, two methods are proposed: 1. Analyze data in real time – uneconomical 2. Stores all data and analyses off-line at low

speed

Two jobs to be done:

Green light on: - determine no. of vehicles moving along particular lanes and their classification by shape and size.

Red light on: - determine the backup length along with the possibility to track its dynamics and classify vehicles in backup.

QUEUE DETECTION ALGORITHM

Spatial-domain technique is used to detect queue – implemented in real-time using low-cost system.

For this purpose two different algorithms have been used:-

Motion detection operation

Vehicle detection operation

QUEUEDETECTION

EDGE DETECTION

APPLICATIONS

video conferencing

augmented reality

context aware computing

video-based interfaces for human-computer interaction

VIDEO CONFERENCING

It is digital compression of audio and video streams in real time.

Video input : video camera or webcam.

Video output: computer monitor television or projector

AUGMENTED REALITY

A combination of a real scene viewed by a user and a virtual scene generated by a computer that augments the scene with additional information.

CONTEXT AWARE COMPUTING

A system is context-aware if it uses context to provide relevant information and/or services to the user, where

relevancy depends on the user’s task.

CONCLUSION RTIP involves many aspects of hardware and

software in order to achieve high resolution input,low latency capture,high performance processing and efficient display.The measure- ment algorithm has been applied to traffic scenes with different lighting conditions. And RTIP be at the heart of many applications.

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