Modeling of temporarily static objects in surveillance video data

  • US 8,744,123 B2
  • Filed: 08/29/2011
  • Issued: 06/03/2014
  • Est. Priority Date: 08/29/2011
  • Status: Active Grant
First Claim
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1. A method for using region-level adaptive background modeling, the method comprising:

  • classifying via a finite state machine device an object blob having a bounding box detected in frame image data of a video data input as a background object, as a moving foreground object, or as a temporally static object, by classifying the object blob as the temporally static object when the detected bounding box is distinguished from a background model of a scene image of the video data input and remains static in the scene image for at least a first threshold period;

    tracking the bounding box of the object blob classified as the temporally static object via the finite state machine device by matching masks of the bounding box in subsequent frame data of the video data input; and

    sub-classifying the tracked temporally static object as within a visible sub-state, as within an occluded sub-state, or as within another sub-state that is not visible and not occluded, as a function of a static value ratio of a total number of pixels overlapped by both the tracked bounding box and a foreground region of the background model over a total number of pixels contained within the tracked bounding box, wherein the foreground region corresponds to the tracked bounding box.

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