MY172143A - Method for non-static foreground feature extraction and classification - Google Patents

Method for non-static foreground feature extraction and classification

Info

Publication number
MY172143A
MY172143A MYPI2012005406A MYPI2012005406A MY172143A MY 172143 A MY172143 A MY 172143A MY PI2012005406 A MYPI2012005406 A MY PI2012005406A MY PI2012005406 A MYPI2012005406 A MY PI2012005406A MY 172143 A MY172143 A MY 172143A
Authority
MY
Malaysia
Prior art keywords
clouds
current frame
feature points
disparity
images
Prior art date
Application number
MYPI2012005406A
Inventor
Binti Kadim Zulaikha
Hock Woon Hon
Binti Samudin Norshuhada
Original Assignee
Mimos Berhad
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Mimos Berhad filed Critical Mimos Berhad
Priority to MYPI2012005406A priority Critical patent/MY172143A/en
Priority to PCT/MY2013/000266 priority patent/WO2014092552A2/en
Publication of MY172143A publication Critical patent/MY172143A/en

Links

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/20—Analysis of motion
    • G06T7/292—Multi-camera tracking
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/194—Segmentation; Edge detection involving foreground-background segmentation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/20—Analysis of motion
    • G06T7/215—Motion-based segmentation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/20—Analysis of motion
    • G06T7/246—Analysis of motion using feature-based methods, e.g. the tracking of corners or segments
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/20—Analysis of motion
    • G06T7/285—Analysis of motion using a sequence of stereo image pairs
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10016—Video; Image sequence
    • G06T2207/10021—Stereoscopic video; Stereoscopic image sequence
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30232—Surveillance

Landscapes

  • Engineering & Computer Science (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Multimedia (AREA)
  • Image Analysis (AREA)

Abstract

The present invention provides a method for processing videos captured through a stereo camera, the videos having a plurality of frames, each frames includes at least a left and a right images. The method includes acquiring videos captured by the pair of stereo cameras (101) and acceleration information of the stereo camera (101) detected through the accelerometer (102); extracting feature points in the right and left images of a current frame; matching the feature points between the right and left images, wherein disparity value of each feature point is computed; clustering the feature points on a current frame into one or more clouds based on their appearance and distance; determining a correspondence between the clouds of the current frame and previous frame; classifying the clouds through estimating statistical disparity distribution of each cloud to identify if the respective clouds are new clouds, existing clouds or unknown clouds; and merging the clouds that are determined to be a same object based on the tracking information and changes of disparity of the clouds over time. A system carrying out the above method is also provided herein.
MYPI2012005406A 2012-12-13 2012-12-13 Method for non-static foreground feature extraction and classification MY172143A (en)

Priority Applications (2)

Application Number Priority Date Filing Date Title
MYPI2012005406A MY172143A (en) 2012-12-13 2012-12-13 Method for non-static foreground feature extraction and classification
PCT/MY2013/000266 WO2014092552A2 (en) 2012-12-13 2013-12-13 Method for non-static foreground feature extraction and classification

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
MYPI2012005406A MY172143A (en) 2012-12-13 2012-12-13 Method for non-static foreground feature extraction and classification

Publications (1)

Publication Number Publication Date
MY172143A true MY172143A (en) 2019-11-14

Family

ID=50179900

Family Applications (1)

Application Number Title Priority Date Filing Date
MYPI2012005406A MY172143A (en) 2012-12-13 2012-12-13 Method for non-static foreground feature extraction and classification

Country Status (2)

Country Link
MY (1) MY172143A (en)
WO (1) WO2014092552A2 (en)

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Also Published As

Publication number Publication date
WO2014092552A3 (en) 2014-09-12
WO2014092552A2 (en) 2014-06-19

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