Adaptive human motion tracking using non-synchronous multiple viewpoint observations

Akira Utsumi*, Howard Yang, Jun Ohya

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)


In this paper, we propose an adaptive human tracking system with non-synchronous multiple observations. Our system consists of three types of processes, discovering node for detecting newly appeared person, tracking node for tracking each target person, and observation node for processing one viewpoint (camera) images. We have multiple observation nodes and each node works fully independently. The tracking node integrates absented information based on reliability evaluation. Both observation conditions (number of cameras, relative distance between a human and cameras, extent of occlusion, etc.) and human motion states (walking, standing, sitting) are considered in the evaluation. Matching between tracking models and observed image features are performed in each observation node based on the position, size and color similarities of each 2D image. Due to the non-synchronous property, this system is highly scalable for increasing the detection area and number of observing nodes. Experimental results for some indoor scenes are also described.

Original languageEnglish
Pages (from-to)607-610
Number of pages4
JournalProceedings - International Conference on Pattern Recognition
Issue number4
Publication statusPublished - 2000 Dec 1
Externally publishedYes

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition


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