Computer Vision System Toolbox™ provides video tracking algorithms, such as continuously adaptive mean shift (CAMShift) and Kanade-Lucas-Tomasi (KLT). You can use these algorithms for tracking a single object or as building blocks in a more complex tracking system. The toolbox also provides a framework for multiple object tracking that includes Kalman filtering and the Hungarian algorithm for assigning object detections to tracks.
|Assign detections to tracks for multiobject tracking|
|Create Kalman filter for object tracking|
|Kalman filter for object tracking|
|Histogram-based object tracking|
|Track points in video using Kanade-Lucas-Tomasi (KLT) algorithm|
|Estimate motion between images or video frames|
|Locate template in image|
Tracking is the process of locating a moving object or multiple objects over time in a video stream.
This example shows how to automatically detect and track a face.
This example shows how to automatically detect and track a face using feature points.
This example shows how to automatically detect and track a face in a live video stream, using the KLT algorithm.
This example shows how to track pedestrians using a camera mounted in a moving car.
This example shows how to use the
vision.KalmanFilter object and
configureKalmanFilter function to track objects.
This example shows how to track objects at a train station and to determine which ones remain stationary.