An Integrated System for Tracking and Recognition using Kalman Filter

被引:0
作者
Vijay, Akhil A. [1 ]
Johnson, Anoop K. [1 ]
机构
[1] Mar Baselios Coll Engn & Technol, Dept Elect & Commun Engn, Thiruvananthapuram, Kerala, India
来源
2014 INTERNATIONAL CONFERENCE ON CONTROL, INSTRUMENTATION, COMMUNICATION AND COMPUTATIONAL TECHNOLOGIES (ICCICCT) | 2014年
关键词
object tracking; recognition; kalman filter; background substraction; video tracking; morphological operations;
D O I
暂无
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Tracking of any object from a video scene becomes more critical not only for security applications but also for analyzing traffic. This system integrates low level image processing as well as high level image processing through online data model and offline data model for more efficiency and robustness. Also this makes the system to handle occlusion conditions and abrupt color intensity variation conditions. This system uses median filtering and blob extraction for moving object detection. The offline model employs high level image processing recognize the moving objects. Here the Kalman filter is used for effective tracking under complex situations.
引用
收藏
页码:1065 / 1069
页数:5
相关论文
共 50 条
[31]   OBJECT DETECTION AND TRACKING USING KALMAN FILTER AND FAST MEAN SHIFT ALGORITHM [J].
Ali, A. ;
Terada, K. .
VISAPP 2009: PROCEEDINGS OF THE FOURTH INTERNATIONAL CONFERENCE ON COMPUTER VISION THEORY AND APPLICATIONS, VOL 1, 2009, :585-589
[32]   Based On Improved Kalman Filter Multiple Vehicles Tracking System [J].
Zhou, Yaling ;
Li, Weihai .
INTERNATIONAL ACADEMIC CONFERENCE ON THE INFORMATION SCIENCE AND COMMUNICATION ENGINEERING (ISCE 2014), 2014, :233-238
[33]   An integrated GPS/INS Navigation System for Small AUVs using an asynchronous Kalman filter [J].
Yun, X ;
Hernandez, GC ;
Bachmann, ER ;
McGhee, RB ;
Healey, AJ .
PROCEEDINGS OF THE 1998 WORKSHOP ON AUTONOMOUS UNDERWATER VEHICLES, (AUV '98), 1998, :43-49
[34]   Comparative Analysis of the Efficiency of the Kalman Filter and Particle Filter in Solving the Problem of Object Tracking in a Seismic Security System [J].
Alyamkin, S. A. ;
Nezhevenko, E. S. .
OPTOELECTRONICS INSTRUMENTATION AND DATA PROCESSING, 2014, 50 (01) :54-60
[35]   Beam Tracking Algorithm for UAV Communications Using Kalman Filter [J].
Song, Ha-Lim ;
Ko, Young-chai ;
Cho, Jungil ;
Hwang, Chanho .
11TH INTERNATIONAL CONFERENCE ON ICT CONVERGENCE: DATA, NETWORK, AND AI IN THE AGE OF UNTACT (ICTC 2020), 2020, :1101-1104
[36]   An improved tracking Kalman filter using a multilayered neural network [J].
Takaba, K ;
Iiguni, Y ;
Tokumaru, H .
MATHEMATICAL AND COMPUTER MODELLING, 1996, 23 (1-2) :119-128
[37]   Vehicle tracking with Kalman filter using online situation assessment [J].
Khalkhali, Maryam Baradaran ;
Vahedian, Abedin ;
Yazdi, Hadi Sadoghi .
ROBOTICS AND AUTONOMOUS SYSTEMS, 2020, 131
[38]   LTE Mobile positioning and tracking simulator using Kalman Filter [J].
El Mourabit, I. ;
Badri, A. ;
Sahel, A. ;
Baghdad, A. .
2016 INTERNATIONAL CONFERENCE ON WIRELESS NETWORKS AND MOBILE COMMUNICATIONS (WINCOM), 2016, :P207-P213
[39]   Tracking Objects through Occlusions Using Improved Kalman Filter [J].
Wang, Jin ;
He, Fei ;
Zhang, Xuejie ;
Gao, Yun .
2ND IEEE INTERNATIONAL CONFERENCE ON ADVANCED COMPUTER CONTROL (ICACC 2010), VOL. 5, 2010, :223-228
[40]   A Novel Approach for Detection and Tracking of Vehicles using Kalman Filter [J].
Srilekha, S. ;
Swamy, G. N. ;
Krishna, A. Anudeep .
2015 INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE AND COMMUNICATION NETWORKS (CICN), 2015, :234-236