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Video Surveillance systems for Traffic Monitoring

Simeon Indupalli

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Presentation Overview

Video surveillance systems. Traffic monitoring issues. Object tracking techniques. Vehicle tracking strategies. A real time system Explanation. Future Work

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what is video surveillance?

Present Implementations? Human detection systems. vehicle monitoring systems.

Advantages of video surveillance? Keep track of information video data for future use. Helpful in identifying people in the crime scenes etc..

Disadvantages of the present system? It’s difficult to maintain heavy amount of raw video data Human interaction. Require higher bandwidth for transmitting the visual data.

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Video surveillance in the context of Computer Vision Detection and tracking of moving objects are the important

tasks of the computer vision. The video surveillance systems not only need to track the

moving objects but also interpret their patterns of behaviours. This means solving the information and integration the pattern.

Advantages Minimizes the user interaction. Less amount of prohibitive bandwidth. Minimizes the cost and time.

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Need for Traffic Monitoring

To reduce the traffic congestion on highways

Reduce the road accidents

Identifying suspicious vehicles. Etc..,

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Traffic Monitoring in Computer Vision The quest for better traffic information, an increasing

reliance on traffic surveillance has resulted in a better vehicle detection.

Taking some intelligent actions based on the conditions.

Traffic scene analysis in 3 categories. A strait forward vehicle detection and

counting system . Congestion monitoring and traffic scene

analysis. Vehicle classification and tracking systems

which involve much more detailed scene traffic analysis.

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Responsibilities of reliable Traffic Monitoring System Adaptive to changes in the real world environments Easy to set up Capable of operating independently of human

operators. Capable of intelligent decisions. Capable of monitoring multiple cameras and

continuous operation.

Reasons for unsuccessful implementation**

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A Traffic Monitoring System

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Object Classification

Shape based classification. Image blob area, blob bounding box Classification based on above info.

Motion-based classification. Human motion shows periodic property. Time frequency analysis applied. Residual flow taken under consideration.

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Object tracking strategies (I)* Background subtraction

Difference between the current image and the reference background image in a pixel by pixel fashion.

Sensitive to the background changes

Wallflower principles for effective background maintenance.

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Object tracking strategies (II) Temporal differencing

Moving objects changes intensity faster than static ones

Uses consecutive frames to identify the difference.

Adaptive to dynamic scene changes

Problems in extracting all relevant features.

Improved versions uses three frames instead of two

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Object tracking strategies (III)

Optical flow To identify characteristics of

flow vectors of moving objects over time.

It’s used to detect independently moving objects in presence of camera.

Requires a specialized hardware to implement.

Optical flow of moving objects

Meyer et al

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Vehicle detection techniques

Model based detection Region based detection Active contour based detection Feature based detection

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Vehicle detection technique (I) Model based Tracking

The emphasis is on recovering trajectories and models with high accuracy for a small number of vehicles.

The most serious weakness of this approach is the reliance on detailed geometric object models.

Disadvantage It is unrealistic to expect detailed

models for all vehicles that could be found on the roadway

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Vehicle detection technique (II) Region based tracking

It detects each vehicle blob using a cross correlation function.

Vehicle detection based on back ground subtraction.

Disadvantage Difficult to detect the vehicles

under congested traffic, because vehicles partly occlude with one another

Potential segmentation problem

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Vehicle detection technique (III) Active contour based

detection Tracking is based on active

contour models, or snakes. Representing object in

bounding contour and keep updating it dynamically.

It reduced computational complexity compared to the region based detection.

Disadvantage: The inability to segment

vehicles that are partially occluded remains a problem.

Bounding counters

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Vehicle detection technique (IV) Feature based detection

Tracks sub-features such as distinguishable points or lines on the object

Effectiveness improved by the addition of common motion constraint.

Features are grouped together based on common motion, avoiding segmentation problem due to occlusion

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A typical vehicle tracking procedure

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Wallflower Principles & Practice of Background Maintenance.

•Moved objects

•Time of day

•Light switch

•Waving trees

•camouflage

•Foreground capture

•Stopped car

•Moving car

•Shadows

•Bootstrapping

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Wallflower: Three levels of abstraction

Pixel level Maintains models of back ground of each individual pixel. Processing makes the preliminary classification between

foreground and background Dynamic to scene changes.

Region level Emphasis is on interrelationship between the pixels Helps to refine raw classification at pixel level

Frame level It watches for the sudden changes in the large parts of the

image and swaps in alternative background models.

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A real time traffic monitoring system

Feature based tracking algorithm

•Camera calibration

•Feature detection

•Vehicle tracking

•Feature grouping

Benjamin Coifman, Jitendra Malik, David Beymer

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Offline camera definition Line correspondences for a projective mapping. A detection region near the image bottom and an

exit region at the image top And multiple fiducial points for camera calibration

Based on the above information the system computes the homography between the image coordinates(x,y) and the world coordinates(X,Y)

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On-line tracking and grouping Detector

Detecting corners at the bottom of image, where brightness varies in more than one direction.

Detection operationalzed by the points in the image I

Tracker Uses kalman filters to predict the velocity

in the next image. Normalized correlation is used to search

the small region of image. Group

Grouper uses common motion constraint. Once all the corner features are identified

they are grouped together. Monitoring the distance between the point

d(t)=P1(t)-p2(t)

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Sample corner features identified by the tracker

Sample feature tracks from the tracker

Sample feature groups from the tracker

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Conclusion & Future Work

The real time traffic surveillance system is still under research due to the background maintenance problem and occlusion.

Better Background maintenance Solving occlusion problem

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References:

A Survey on visual surveillance of object motion and behaviour

– HU et al Transportation research part-c/ A real time computer vision system for

Traffic monitoring and vehicle tracking – B.coifman, J.Malik etc.. Steps towards cognitive vision system – H.Nagel, IAKS Karlsruhe. VSAM project – Carneigh Mellon University Wallflower Principles and practices – Microsoft Research group.