Moving object tracking using a Kalman filter - Embedded.com

Moving object tracking using a Kalman filter

Visual surveillance systems are widely used to monitor security sensitive areas. And the availability of high- powered computers, high quality video cameras, and the increasing need for automated video analysis has generated a great deal of interest in object tracking algorithms.

There are three key steps in video analysis: detection of interesting moving objects, tracking of such objects from frame to frame, and analysis of object tracks to recognize their behavior.

Video tracking can be defined as an action which can estimate the trajectory of an object in the image plane as it moves within a scene. A tracker assigns consistent labels to the tracked objects in different frames of a video. Tracking of an object can be done by continuously detecting to localize regions, points or features of an image frame by frame.

In this paper we describe a surveillance system can be used to detect and track the moving objects. First phase of the system is to detect the moving objects in the video. Second phase of the system will track the detected object.

The detection of the moving object has been done using simple background subtraction and tracking of single moving object has been done using Kalman filter.

The algorithm has been applied successfully on standard surveillance video datasets taken using still cameras, which are located in indoor as well as outdoor environment having moderate to complex environments.

To read this external content in full, download the complete paper from the author online archives.

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