Object Detection via Heterogeneous Feature Clustering

  • 林 郁文

學生論文: Master's Thesis

摘要

Automated object detection schemes are essential in analyzing surveillance videos However for a large surveillance system with numerous cameras supervised object detection methods require a lengthy training process In this paper we propose an unsupervised approach for identifying different foreground objects Foreground objects are extracted from videos and represented by heterogeneous features To assess similarities among feature vectors with unequal lengths a sequence matching procedure is proposed Then different properties of foreground objects are identified by clustering feature vectors in each feature space Finally a tree-structured clustering algorithm is used to identify the foreground objects with similar properties The results of real data set confirm the effectiveness of the proposed algorithm in separating different foreground objects
獎項日期2014 七月 28
原文English
監督員Pau-Choo Chung (Supervisor)

引用此文

Object Detection via Heterogeneous Feature Clustering
郁文, 林. (Author). 2014 七月 28

學生論文: Master's Thesis