06-04-2016, 09:47 AM
matlab source code for vehicle detection
This example shows how to detect and count cars in a video sequence using foreground detector based on Gaussian mixture models (GMMs).
Introduction
Detecting and counting cars can be used to analyze traffic patterns. Detection is also a first step prior to performing more sophisticated tasks such as tracking or categorization of vehicles by their type.
This example shows how to use the foreground detector and blob analysis to detect and count cars in a video sequence. It assumes that the camera is stationary. The example focuses on detecting objects. To learn more about tracking objects, see the example titled Motion-Based Multiple Object Tracking.
Step 1 - Import Video and Initialize Foreground Detector
Rather than immediately processing the entire video, the example starts by obtaining an initial video frame in which the moving objects are segmented from the background. This helps to gradually introduce the steps used to process the video.
The foreground detector requires a certain number of video frames in order to initialize the Gaussian mixture model. This example uses the first 50 frames to initialize three Gaussian modes in the mixture model.
foregroundDetector = vision.ForegroundDetector('NumGaussians', 3, ...
'NumTrainingFrames', 50);
videoReader = vision.VideoFileReader('visiontraffic.avi');
for i = 1:150
frame = step(videoReader); % read the next video frame
foreground = step(foregroundDetector, frame);
end
This example shows how to detect and count cars in a video sequence using foreground detector based on Gaussian mixture models (GMMs).
Introduction
Detecting and counting cars can be used to analyze traffic patterns. Detection is also a first step prior to performing more sophisticated tasks such as tracking or categorization of vehicles by their type.
This example shows how to use the foreground detector and blob analysis to detect and count cars in a video sequence. It assumes that the camera is stationary. The example focuses on detecting objects. To learn more about tracking objects, see the example titled Motion-Based Multiple Object Tracking.
Step 1 - Import Video and Initialize Foreground Detector
Rather than immediately processing the entire video, the example starts by obtaining an initial video frame in which the moving objects are segmented from the background. This helps to gradually introduce the steps used to process the video.
The foreground detector requires a certain number of video frames in order to initialize the Gaussian mixture model. This example uses the first 50 frames to initialize three Gaussian modes in the mixture model.
foregroundDetector = vision.ForegroundDetector('NumGaussians', 3, ...
'NumTrainingFrames', 50);
videoReader = vision.VideoFileReader('visiontraffic.avi');
for i = 1:150
frame = step(videoReader); % read the next video frame
foreground = step(foregroundDetector, frame);
end
