Moving Object Detection from Moving Camera Image Sequences Using an Inertial Measurement Unit Sensor

被引:10
作者
Jung, Sukwoo [1 ]
Cho, Youngmok [1 ]
Kim, Doojun [1 ]
Chang, Minho [1 ]
机构
[1] Korea Univ, Dept Mech Engn, Seoul 02841, South Korea
来源
APPLIED SCIENCES-BASEL | 2020年 / 10卷 / 01期
关键词
moving camera; motion estimation; moving object detection; image processing; VEHICLE DETECTION; TRACKING; ROAD;
D O I
10.3390/app10010268
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
This paper describes a new method for the detection of moving objects from moving camera image sequences using an inertial measurement unit (IMU) sensor. Motion detection systems with vision sensors have become a global research subject recently. However, detecting moving objects from a moving camera is a difficult task because of egomotion. In the proposed method, the interesting points are extracted by a Harris detector, and the background and foreground are classified by epipolar geometry. In this procedure, an IMU sensor is used to calculate the initial fundamental matrix. After the feature point classification, a transformation matrix is obtained from matching background feature points. Image registration is then applied to the consecutive images, and a difference map is extracted to find the foreground region. Finally, a minimum bounding box is applied to mark the detected moving object. The proposed method is implemented and tested with numerous real-world driving videos, which show that it outperforms the previous work.
引用
收藏
页数:13
相关论文
共 35 条
  • [21] A Kalman Filter-Based Algorithm for IMU-Camera Calibration: Observability Analysis and Performance Evaluation
    Mirzaei, Faraz M.
    Roumeliotis, Stergios I.
    [J]. IEEE TRANSACTIONS ON ROBOTICS, 2008, 24 (05) : 1143 - 1156
  • [22] Object-Based Approach for Adaptive Source Coding of Surveillance Video
    Pan, Tung-Ming
    Fan, Kuo-Chin
    Wang, Yuan-Kai
    [J]. APPLIED SCIENCES-BASEL, 2019, 9 (10):
  • [23] A New Result on Average Consensus for Multiple Agents with Switching Topology and Communication Delay
    Qin, Jiahu
    Gao, Huijun
    Zheng, Wei Xing
    [J]. PROCEEDINGS OF THE 48TH IEEE CONFERENCE ON DECISION AND CONTROL, 2009 HELD JOINTLY WITH THE 2009 28TH CHINESE CONTROL CONFERENCE (CDC/CCC 2009), 2009, : 3703 - 3708
  • [24] Looking at Vehicles on the Road: A Survey of Vision-Based Vehicle Detection, Tracking, and Behavior Analysis
    Sivaraman, Sayanan
    Trivedi, Mohan Manubhai
    [J]. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2013, 14 (04) : 1773 - 1795
  • [25] On-road vehicle detection: A review
    Sun, ZH
    Bebis, G
    Miller, R
    [J]. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2006, 28 (05) : 694 - 711
  • [26] Deep Fusion Feature Based Object Detection Method for High Resolution Optical Remote Sensing Images
    Wang, Eric Ke
    Li, Yueping
    Nie, Zhe
    Yu, Juntao
    Liang, Zuodong
    Zhang, Xun
    Yiu, Siu Ming
    [J]. APPLIED SCIENCES-BASEL, 2019, 9 (06):
  • [27] Wu SD, 2011, IEEE I CONF COMP VIS, P1419, DOI 10.1109/ICCV.2011.6126397
  • [28] An Enhanced Feature Pyramid Object Detection Network for Autonomous Driving
    Wu, Yutian
    Tang, Shuming
    Zhang, Shuwei
    Ogai, Harutoshi
    [J]. APPLIED SCIENCES-BASEL, 2019, 9 (20):
  • [29] New trends on moving object detection in video images captured by a moving camera: A survey
    Yazdi, Mehran
    Bouwmans, Thierry
    [J]. COMPUTER SCIENCE REVIEW, 2018, 28 : 157 - 177
  • [30] Yu Y, 2019, INT J CONTROL AUTOM, V17, P1866