Segmentation by temporal detection integration

Yi Ying Wang*, Chia-Han Lee

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


We propose a novel method, called segmentation by temporal detection integration (STDI), to improve the segmentation results of background subtraction. The STDI applies split and merge algorithms forwardly and backwardly to obtain appropriate region segmentations based on the integration of the temporal detections across frames. The proposed scheme can be applied to human detection and tracking to avoid the situations that a group of people being detected as one single segment or a human body being separated into fragments, thus making the subsequent human tracking more reliable. To verify the performance of the proposed STDI, a dynamic layer model is used to depict motion, appearance, and shape of detections for human tracking. Experimental results show that the proposed STDI significantly improves the background subtraction segmentation results, and therefore, the accuracy of human tracking.

Original languageEnglish
Title of host publicationICIP 2011
Subtitle of host publication2011 18th IEEE International Conference on Image Processing
Number of pages4
StatePublished - 1 Dec 2011
Event2011 18th IEEE International Conference on Image Processing, ICIP 2011 - Brussels, Belgium
Duration: 11 Sep 201114 Sep 2011

Publication series

NameProceedings - International Conference on Image Processing, ICIP
ISSN (Print)1522-4880


Conference2011 18th IEEE International Conference on Image Processing, ICIP 2011


  • Background subtraction
  • dynamic layer model
  • human tracking
  • temporal detection integration

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