Maximally Stable Extremal Regions Improved Tracking Algorithm Based on Depth Image

被引:0
作者
Wang, Haikuan [1 ]
Xie, Dong [1 ]
Sun, Haoxiang [1 ]
Zhou, Wenju [1 ]
机构
[1] Shanghai Univ, Sch Mech Engn & Automat, 99 Shangda Rd, Shanghai 200444, Peoples R China
来源
INTELLIGENT COMPUTING AND INTERNET OF THINGS, PT II | 2018年 / 924卷
关键词
Depth image; MSER algorithm; Target tracking; Camshift algorithm;
D O I
10.1007/978-981-13-2384-3_51
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
In order to solve the problem that traditional Camshift algorithm can easily fail to track overlapping targets and multiple similar depth targets, a new improved maximally stable extremal regions (MSER) algorithm is presented in this paper. Firstly, the suspected target contour is extracted and similarity analysis is performed. Secondly, the improved MSER algorithm is used to confirm the target contour and update the similarity library. Finally, combined with the physical properties unique to the depth image and based on the Kalman filter, it is possible to predict the tracking target's moving position. The experimental results show that the real-time performance and recognition rate are improved, and robustness to the situation of target overlap and occlusion is better with the improved MSER algorithm.
引用
收藏
页码:546 / 554
页数:9
相关论文
共 14 条
  • [11] Efficient Human Pose Estimation from Single Depth Images
    Shotton, Jamie
    Girshick, Ross
    Fitzgibbon, Andrew
    Sharp, Toby
    Cook, Mat
    Finocchio, Mark
    Moore, Richard
    Kohli, Pushmeet
    Criminisi, Antonio
    Kipman, Alex
    Blake, Andrew
    [J]. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2013, 35 (12) : 2821 - 2840
  • [12] New method of structure light measurement system calibration based on adaptive and effective evaluation of 3D-phase distribution
    Sitnik, R
    [J]. Optical Measurement Systems for Industrial Inspection IV, Pts 1 and 2, 2005, 5856 : 109 - 117
  • [13] Sun C, 1997, DIGITAL IMAGE COMPUT, P95
  • [14] Nguyen T, 2017, 2017 14TH CONFERENCE ON COMPUTER AND ROBOT VISION (CRV 2017), P321, DOI [10.1109/IPCon.2017.8116125, 10.1109/CRV.2017.19]