Monthly Archives: January 2014

Measures of effective video tracking

Measures of effective video tracking

  • Tahir Nawaz, Fabio Poiesi, and Andrea Cavallaro. Measures of effective video tracking. 2014. Open source software
    [BibTeX] [Download software]
    @Misc{2014-01-NAWAZ-2,
    title = {Measures of effective video tracking},
    software = {http://www.eecs.qmul.ac.uk/~andrea/mtte.html},
    author = {Tahir Nawaz and Fabio Poiesi and Andrea Cavallaro},
    organization = {{Queen Mary University of London}},
    note = {Open source software},
    date = {2014-01},
    year = {2014}
    }

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Compiler Feedback using Continuous Dynamic Compilation during Development

Compiler Feedback using Continuous Dynamic Compilation during Development

  • Nicklas Bo Jensen, Christian W. Probst, and Sven Karlsson. Compiler Feedback using Continuous Dynamic Compilation during Development. In Proceedings of the Workshop on Dynamic Compilation Everywhere (DCE), Vienna, Austria, 2014.
    [BibTeX] [Abstract] [Download PDF]

    Optimizing compilers are vital for performance. However, compilers ability to optimize aggressively is limited in some cases. To address this limitation, we have developed a compiler guiding the programmer in making small source code changes, potentially making the source code more amenable to optimization. This tool can help programmers understand what the optimizing compiler has done and suggest automatic source code changes in cases where the compiler refrains from optimizing. We have integrated our tool into an integrated development environment, interactively giving feedback as part of the programmers development flow. We have evaluated our preliminary implementation and show it can guide to a 12% improvement in performance. Furthermore the tool can be used as an interactive optimization adviser improving the performance of the code generated by a production compiler. Here it can lead to a 153% improvement in performance, indicating the feasibility of the tool as a performance adviser for a production compiler.

    @InProceedings{2014-01-JENSEN,
    author = {Nicklas Bo Jensen and Christian W. Probst and Sven Karlsson},
    title = {{Compiler Feedback using Continuous Dynamic Compilation during Development}},
    booktitle = {{Proceedings of the Workshop on Dynamic Compilation Everywhere (DCE)}},
    address = {Vienna, Austria},
    date = {2014-01-21},
    url = {https://sites.google.com/site/dynamiccompilationeverywhere/home/dce-2014},
    abstract = {Optimizing compilers are vital for performance. However, compilers ability to optimize aggressively is limited in some cases. To address this limitation, we have developed a compiler guiding the programmer in making small source code changes, potentially making the source code more amenable to optimization.
    This tool can help programmers understand what the optimizing compiler has done and suggest automatic source code changes in cases where the compiler refrains from optimizing. We have integrated our tool into an integrated development environment, interactively giving feedback as part of the programmers development flow. We have evaluated our preliminary implementation and show it can guide to a 12% improvement in performance. Furthermore the tool can be used as an interactive optimization adviser improving the performance of the code generated by a production compiler. Here it can lead to a 153% improvement in performance, indicating the feasibility of the tool as a performance adviser for a production compiler.},
    year = {2014}
    }

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Measures of effective video tracking

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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