A method and apparatus for real-time tracking of a non-rigid target. The tracking is based on visual features, such as color and/or texture, where statistical distributions of those features characterize the target. A degree of similarity (.rho.(y.sub.0)) is computed between a given target (at y.sub.0) in a first frame and a candidate target (at y.sub.1) in a successive frame, the degree being expressed by a metric derived from the Bhattacharyya coefficient. A gradient vector corresponding to a maximization of the Bhattacharyya coefficient is used to derive the most probable location of the candidate target in the successive frame.
An image processing system processes a sequence of images to generate a statistical model for each of a number of different persons to be tagged so as to be identifiable in subsequent images. The statistical model for a given tagged person incorporates at least one appearance feature, such as color, texture, etc., and at least one geometric feature, such as shape or position of a designated region of similar appearance within one or more images. The models are applied to subsequent images in order to perform a person detection, person location and/or person tracking operation. An action of the image processing system is controlled based on a result of the operation.
Tracking and surveillance methods and systems for monitoring objects passing in front of non-overlapping cameras. Invention finds corresponding tracks from different cameras and works out which object passing in front of the camera(s) made the tracks, in order to track the object from camera to camera. The invention uses an algorithm to learn inter-camera spatial temporal probability using Parzen windows, learns inter-camera appearance probabilities using distribution of Bhattacharyya distances between appearance models, establishes correspondences based on Maximum A Posteriori (MAP) framework combining both spatial temporal and appearance probabilities, and updates learned probabilities throughout the lifetime of the system.
An image processing system processes a sequence of images to generate a statistical model for each of a number of different persons to be tagged so as to be identifiable in subsequent images. The statistical model for a given tagged person incorporates at least one appearance feature, such as color, texture, etc., and at least one geometric feature, such as shape or position of a designated region of similar appearance within one or more images. The models are applied to subsequent images in order to perform a person detection, person location and/or person tracking operation. An action of the image processing system is controlled based on a result of the operation.
The present invention discloses an object-detection method and a multi-class Bhattacharyya Boost algorithm used therein, wherein firstly, integral images are calculated from an image data in order to speed up the extraction of the characteristics of the objects; then, multiple rectangles of different sizes are scanned at different locations of the image data, and the multi-class Bhattacharyya Boost algorithm is used to detect multi-class objects. In the present invention, the detection framework can use only one single boosted cascade to determine the status and position of the object inside the image data. The simultaneous multi-class detection of the present invention can effectively overcome the detection difficulties resulting from the diversification of object appearances under different conditions. Therefore, the present invention can detect multiple objects simultaneously and can also detect one object having multiple classes of appearances, and further, the detection speed and the system robustness of the present invention are superior to those of the conventional technologies.
