jaenudin86 / vehicletrafficdetction

Video/Image processing project using Blob Detection.

vehicletrafficdetction

#This is and Video/Image processing project using Blob Detection.

1 Project Idea: Tra c congestion is the major problem in the whole world. Increase in tra c leads to slow down the city. Due to increase in tra c, chances of accident increases. So in order to reduce tra c in the city, a tra c surveillance system is proposed which will help in reducing the tra c congestion. The proposed system implements real time road tra c analyzer in order to reduce waiting time on road tra c by counting number of vehicles with the help of blob algorithm. The blob algorithm counts number of vehicles on road and will display according to the tra c density. Proposed system includes computer vision system to count number of vehicles on the road. The system involves analyzing a sequence of road images which represent the ow of tra c for the given time period and place. The approach utilized to analyze tra c videos using the modules like background subtraction, blob detection, bounding box, blob tracking and vehicle counting.

2 Motivation of the Project In this urban life transportation is very common. A lot of miss happenings occur on the road every day .Therefore the need of security and monitoring is developed. To resolve such problems, a system is developed using GPS technologies. GPS modules are popularly used for navigation, positioning, time and other purposes. GPS antenna receives the location values from the satellites. GPS gives information about:

1. Message transmission time

2. Position at that time. Tra c detection module gives exact number of vehicles on road for tra c analysis. Which uses video of tra c at particular location. Video camera is a promising tra c sensor because of its low cost and its potential ability to collect a large amount of information. As the key goal for a tra c surveillance system, the evaluation of tra c conditions can be rep-resented by the following parameters: tra c ow rate, average tra c speed, the length of queue and tra c density. In this, we describe a computer vision system to count vehicles waiting at signal. The approach utilized to analyze tra c videos using the following module pipeline:

3. Background Subtraction

4. Blob Detection

5. Blob Analysis.

6. Blob Tracking.

7. Vehicle Counting

OpenCV: The OpenCV libraries, distributed by us, on the Microsoft Windows operating system are in a Dynamic Linked Libraries (DLL). These have the advantage that all the content of the library are loaded only at runtime, on demand, and that countless programs may use the same library le. This means that if you have ten applications using the OpenCV library, no need to have around a version for each one of them. Of course you need to have the dll of the OpenCV on all systems where you want to run your application. EmguCV: It is a cross platform .Net wrapper to the OpenCV image processing library. Allowing OpenCv functions to be called from .NET compatible languages such as C, VB, VC++, IronPython etc. The wrapper can be compiled by Visual Studio, Xamarin Studio and Unity, it can run on Windows, Linux, Mac OS X, iOS, Android and Windows Phone.

Publication URL http://www.eijo.in/journals/article_detail/106