Cost-Effective Ground-Moving Object Detection Method in Aerial Video by Change Detection of Delaunay Triangulation

Author:

Chen Chao-Ho1ORCID,Huang Deng-Yuan2ORCID,Su Yi-Jen3ORCID,Lin Chia-En1ORCID,Chen Tsong-Yi1ORCID,Jiao Zai-Ci1ORCID,Wang Da-Jinn4ORCID,Wen Cheng-Kang5ORCID

Affiliation:

1. Department of Electronic Engineering, National Kaohsiung University of Science and Technology, 415 Jiangong Rd., Sanmin Dist., Kaohsiung 807618, Taiwan, R.O.C.

2. Department of Electrical Engineering, Da-Yeh University, 168 University Rd., Dacun, Changhua 515, Taiwan, R.O.C.

3. Department of Computer Science and Information Engineering, National Penghu University of Science and Technology, No. 300, Liuhe Rd., Magong City, Penghu County 880011, Taiwan, R.O.C.

4. Department of Business Computing, National Kaohsiung University of Science and Technology, No. 142, Haijhuan Rd., Nanzih Dist., Kaohsiung 81157, Taiwan, R.O.C.

5. Department of Information Management, Tainan University of Technology, 529 Zhongzheng Rd., Yongkang District, Tainan 71002, Taiwan, R.O.C.

Abstract

This paper is dedicated to developing a cost-effective ground-moving object detection method in aerial videos. Without limitations on types, quantity, and distribution of moving objects, which were required by the previous methods, the proposed approach can detect various ground-moving objects in aerial videos captured through diverse flying states in various ground appearances. The proposed method is mainly composed of generation of the appropriate feature points, detection of moving objects, and target tracking. In the originality of our work, a novel detection strategy designed for ground-moving objects is based on change detection of Delaunay triangulation (CDDT) and a three-step motion vector search-based tracking algorithm is further exploited for enhancing the detection rate. Experimental results show that our method can achieve the detection rate of at least 95% (roughly similar to the famous existing state-of-the-art methods) and 0.03 second/frame (far less than the famous existing methods) using test videos (containing only several moving objects distributed in a sparse space) in the previous methods compared. Besides, the average detection rate of 86.81%, average false detection rate of 9.44%, and a frame rate of about 33[Formula: see text]fps can be obtained using our test videos captured in the complicated ground appearances. This result makes the proposed method more attractive for detecting various ground-moving objects in aerial videos, when compared to other approaches, and can also achieve cost-effective performance.

Funder

VIVOTEK INC.

Ministry of Science and Technology, Taiwan

Publisher

World Scientific Pub Co Pte Ltd

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