19-04-2017, 09:37 AM
Target tracking can be described as the process of determining the location of a target characteristic in a sequence of images over time. It is one of the most important applications of sequential state estimation, which naturally supports the Kalman filter and particle filter as the main candidate. It captures significant attention over the last few years because of its crucial value in visual applications, including augmented reality, user perceptual surveillance, object-based video compression, intelligent driver assistance and intelligent freeways, etc. In recent years there has been a lot of work on tracking moving objects within a scene. Systems developed for such tasks as people who follow the face tracking and vehicle tracking have come in many shapes or sizes.
The Kalman filter has many uses, including applications in control, navigation, computer vision and time series econometrics. This example illustrates how to use the Kalman filter to track objects and focuses on three important features:
• Predicting the future location of the object
• Reduction of noise introduced by inaccurate detections
• Facilitate the process of association of multiple objects to their tracks
The Kalman filter has many uses, including applications in control, navigation, computer vision and time series econometrics. This example illustrates how to use the Kalman filter to track objects and focuses on three important features:
• Predicting the future location of the object
• Reduction of noise introduced by inaccurate detections
• Facilitate the process of association of multiple objects to their tracks