Method for Detecting Objects Left-Behind in a Scene
First Claim
1. A method for detecting an object left-behind in a scene, comprising the steps of:
- updating a set of background models using a sequence of images acquired of a scene by a camera, each background model updated at a different temporal scales ranging from short term to long term;
determining a foreground mask from each background model after the updating for a particular image of the sequence;
updating a motion image from the set of foreground masks, in which each pixel in the motion image has an associated evidence value; and
comparing the evidence values with a evidence threshold to detect and signal an object left behind in the scene.
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Abstract
A method detects an object left-behind in a scene by updating a set of background models using a sequence of images acquired of the scene by a camera. Each background model is updated at a different temporal scales ranging from short term to long term. A foreground mask is determined from each background model after the updating for a particular image of the sequence. A motion image is updated from the set of foreground masks. In the motion, image, each pixel has an associated evidence value. The evidence values are compared with a evidence threshold to detect and signal an object left behind in the scene.
85 Citations
25 Claims
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1. A method for detecting an object left-behind in a scene, comprising the steps of:
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updating a set of background models using a sequence of images acquired of a scene by a camera, each background model updated at a different temporal scales ranging from short term to long term; determining a foreground mask from each background model after the updating for a particular image of the sequence; updating a motion image from the set of foreground masks, in which each pixel in the motion image has an associated evidence value; and comparing the evidence values with a evidence threshold to detect and signal an object left behind in the scene. - View Dependent Claims (2, 3, 4, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 20, 23, 24, 25)
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- 5. The method of claim 5, in which the temporal scale is a learning rate at which parameters of the set of background models are updated.
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19. The method of claim 18, in which the evidence threshold maxe is in a range of [10, 300] successive images in the sequence.
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22. The method of claim 18, in which a number of layers in each set is adapted according to the confidence score.
Specification