Performance Evaluation Metrics for Video Tracking

被引:5
|
作者
Sebastian, Patrick [1 ,2 ]
Voon, Yap Vooi [3 ]
Comley, Richard [4 ]
机构
[1] Univ Teknol PETRONAS, Dept Elect, Tronoh, Malaysia
[2] Univ Teknol PETRONAS, Dept Elect Eng, Tronoh, Malaysia
[3] Univ Tunku Abdul Rahman, Dept Elect Eng, Kampar, Perak, Malaysia
[4] Middlesex Univ, Sch Engn & Informat Sci, London NW4 4B, England
关键词
Color space; Tracking metrics; Video surveillance; Video tracking;
D O I
10.4103/0256-4602.90759
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Various tracking methods have been developed to track objects with different degrees or levels of tracking ability. The ability or performance of each tracking method is dependent on the feature or data that is being used for tracking purpose. The ability of a tracking method can be measured by utilizing tracking metrics to give an indication of the tracking ability of an algorithm. This paper offers some insights into the issues and similarities of performance measurement reporting of video tracking algorithms and proposes a method in assessing the robustness of a video tracking algorithm. The proposed metric introduces another measure to measure the consistency of a tracking algorithm. The work presented in this paper shows that using only one metric to measure the tracking performance is inadequate. The proposed metric presented in this paper shows that the utilization of multiple metrics such as tracking success rate and tracking consistency or robustness would give a better indication of the tracking ability of a tracking algorithm used in video surveillance.
引用
收藏
页码:493 / 502
页数:10
相关论文
共 50 条
  • [1] Metrics for performance evaluation of video object segmentation and tracking without ground-truth
    Erdem, CE
    Tekalp, AM
    Sankur, B
    2001 INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, VOL II, PROCEEDINGS, 2001, : 69 - 72
  • [2] Framework for Performance Evaluation of Face, Text, and Vehicle Detection and Tracking in Video: Data, Metrics, and Protocol
    Kasturi, Rangachar
    Goldgof, Dmitry
    Soundararajan, Padmanabhan
    Manohar, Vasant
    Garofolo, John
    Bowers, Rachel
    Boonstra, Matthew
    Korzhova, Valentina
    Zhang, Jing
    IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2009, 31 (02) : 319 - 336
  • [3] Performance evaluation metrics for motion detection and tracking
    Schlögl, T
    Beleznai, C
    Winter, M
    Bischof, H
    PROCEEDINGS OF THE 17TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION, VOL 4, 2004, : 519 - 522
  • [4] Performance Evaluation Metrics and Approaches for Target Tracking: A Survey
    Song, Yan
    Hu, Zheng
    Li, Tiancheng
    Fan, Hongqi
    SENSORS, 2022, 22 (03)
  • [5] Performance evaluation of object detection and tracking in video
    Manohar, V
    Soundararajan, P
    Raju, H
    Goldgof, D
    Kasturi, R
    Garofolo, J
    COMPUTER VISION - ACCV 2006, PT II, 2006, 3852 : 151 - 161
  • [6] Performance evaluation of text detection and tracking in video
    Manohar, V
    Soundararajan, P
    Boonstra, M
    Raju, H
    Goldgof, D
    Kasturi, R
    Garofolo, J
    DOCUMENT ANALYSIS SYSTEMS VII, PROCEEDINGS, 2006, 3872 : 576 - 587
  • [7] MONCE Tracking Metrics: a comprehensive quantitative performance evaluation methodology for object tracking
    Rapko, Kenneth
    Xie, Wanlin
    Walsh, Andrew
    AUTOMATIC TARGET RECOGNITION XXXII, 2022, 12096
  • [8] Metrics for Performance Evaluation of Elliptic Extended Object Tracking Methods
    Yang, Shishan
    Baum, Marcus
    Granstrom, Karl
    2016 IEEE INTERNATIONAL CONFERENCE ON MULTISENSOR FUSION AND INTEGRATION FOR INTELLIGENT SYSTEMS (MFI), 2016, : 523 - 528
  • [9] Auto-video tracking system: Performance evaluation
    Chauhan, B. S.
    Singh, Manvendra
    Sharma, V. K.
    Pandey, P. C.
    DEFENCE SCIENCE JOURNAL, 2008, 58 (04) : 565 - 572
  • [10] An evaluation of video fidelity metrics
    Yu, A
    Lee, R
    Flynn, M
    IEEE COMPCON 97, PROCEEDINGS, 1997, : 49 - 55