This document proposes a genetic algorithm-based moving object detection scheme for real-time traffic surveillance. It uses a genetic dynamic saliency map with background subtraction to detect moving objects with less computation and higher accuracy. The algorithm aims to address challenges with detection of multiple moving objects, size variation, illumination changes, shadows and occlusions in embedded systems with limited resources.

1. A Genetic Algorithm-Based
Moving Object Detection For
Real-Time Traffic Surveillance

2. Surveillance System using genetic algorithm
based moving object detection.

3. Moving object detection scheme for
surveillance.
Genetic Dynamic Saliency Map with
Background Subtraction algorithm.
Less computation and higher detection
accuracy rate.

4.  Moving object detection based on images obtained from fixed CCTV
cameras In real-time embedded systems where the computational and
memory resources are scarce,
 Temporal differencing uses the pixel wise differences between two or
three consecutive images in an image sequence to extract moving regions
and are highly adaptive to the dynamic scene changes.
 It accomplishes this task by extracting and modelling each pixel value
independently through a mixture of Gaussians of a particular allocation.
 The pixels which are determined as belonging to the background category
within the current frame are then described in the distribution

5.  Algorithms based on temporal difference fail to extract all
the relevant pixels of a foreground object especially when
the object has uniform texture or moves slowly.
 The computational complexity of different algorithms such
as object detection is also a challenging task ,when
1) unexpected number of multiple moving objects
2) size variation and poorly textured objects
3) rapid change in illumination conditions
4) shadows and multiple occlusions

6. GDSM and Background subtraction for detecting
moving objects in surveillance.
Background Subtraction is carried out using the
threshold obtained from the object candidates
using the GDSM.
CSD is used to reduce The blur noise around
objects, generated by the repetitive image
resizing in the Gaussian Pyramid the weights of
CSD are optimized using GA, resulting in tighter
object regions

7. Object detection algorithms intended for
embedded systems should be fast, simple and
Effective with superior performance.
Object detection algorithms intended for
embedded systems should be fast, simple and
effective with superior performance.
Gaussians focuses on robust background
modeling and updating to adapt the background
model to the varying illumination conditions
during different times of the day, geometry
reconfiguration of the background structure

9.  Software Requirement:
 Language - Java (JDK 1.7), JSP.
 OS - Windows 7 Ultimate 32-bit.
 Database - MYSQL 5.0,SQLYOG
 Apache Tomcat 7
 Eclipse or NetBeans IDE 7.1.2
 Hardware Requirement :
 1 GB RAM
 80 GB Hard Disk
 Above 2GHz Processor
 Internet connection
 Data Card