Measures of effective video tracking

  • Tahir Nawaz, Fabio Poiesi, and Andrea Cavallaro. Measures of effective video tracking. IEEE Transactions on Image Processing, 23(1):5-43, 2014. doi:10.1109/TIP.2013.2288578
    [BibTeX] [Abstract]

    To evaluate multitarget video tracking results, one needs to quantify the accuracy of the estimated target-size and the cardinality error as well as measure the frequency of occurrence of ID changes. In this paper, we survey existing multitarget tracking performance scores and, after discussing their limitations, we propose three parameter-independent measures for evaluating multitarget video tracking. The measures consider target-size variations, combine accuracy and cardinality errors, quantify long-term tracking accuracy at different accuracy levels, and evaluate ID changes relative to the duration of the track in which they occur. We conduct an extensive experimental validation of the proposed measures by comparing them with existing ones and by evaluating four state-of-the-art trackers on challenging real-world publicly-available data sets. The software implementing the proposed measures is made available online to facilitate their use by the research community.

    @Article{2014-01-NAWAZ-1,
    author = {Tahir Nawaz and Fabio Poiesi and Andrea Cavallaro},
    journal = {{IEEE Transactions on Image Processing}},
    title = {Measures of effective video tracking},
    date = {2014-01-01},
    volume = {23},
    pages = {5-43},
    number = {1},
    doi = {10.1109/TIP.2013.2288578},
    abstract = {To evaluate multitarget video tracking results, one needs to quantify the accuracy of the estimated target-size and the cardinality error as well as measure the frequency of occurrence of ID changes. In this paper, we survey existing multitarget tracking performance scores and, after discussing their limitations, we propose three parameter-independent measures for evaluating multitarget video tracking. The measures consider target-size variations, combine accuracy and cardinality errors, quantify long-term tracking accuracy at different accuracy levels, and evaluate ID changes relative to the duration of the track in which they occur. We conduct an extensive experimental validation of the proposed measures by comparing them with existing ones and by evaluating four state-of-the-art trackers on challenging real-world publicly-available data sets. The software implementing the proposed measures is made available online to facilitate their use by the research community.},
    year = {2014}
    }

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