Real-Time Image Processing Applied To Traffic –Queue Detection Algorithm

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ABSTRACT
This paper primarily aims at the new technique of video image processing used to
solve problems associated with the real-time road traffic control systems. There is a growing
demand for road traffic data of all kinds. Increasing congestion problems and problems
associated with existing detectors spawned an interest in such new vehicle detection
technologies. But the systems have difficulties with congestion, shadows and lighting
transitions.



INTRODUCTION
• increasing demand for road traffic data of all sorts
• variation of parameters in real-world traffic
3
• aimed to measure queue parameters accurately
• algorithm has two operations : vehicle detection and motion detection
• operations applied to profiles consisting sub-profiles to detect queue
• motion detection is based on applying a differencing technique on the profiles of the images
along the road
• the vehicle detection is based on applying edge detection on these profiles
Image processing applied to traffic
Need for processing of traffic data: -
Traffic surveillance and control, traffic management, road safety and development of
transport policy.
Traffic parameters measurable: -
Traffic volumes, Speed, Headways, Inter-vehicle gaps, Vehicle classification, Origin and
destination of traffic, Junction turning.



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.
Pipeline Preprocessing does this job
Stages in Pipeline Preprocessing :
(1) Spatial Averaging – contiguous pixels are averaged (convolution)
(2) Subtraction of background scene from incoming picture.
(3) Threshold – Large diff.s are true ‘1’, small diff.s are false ‘0’
(4) Data Compression – reduces resulting data.
(5) Block buffering – collects data into blocks.
(6) Tape Interface – blocks are loaded onto a digital cassette recorder
Preprocessed picture is submitted to processor as 2-D array of no.s.