A Thermal Infrared Video Benchmark for Visual Analysis

Zheng Wu, Nathan Fuller, Diane Theriault, Margrit Betke
<span title="">2014</span> <i title="IEEE"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/ilwxppn4d5hizekyd3ndvy2mii" style="color: black;">2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops</a> </i> &nbsp;
We hereby publish a new thermal infrared video benchmark, called TIV, for various visual analysis tasks, which include single object tracking in clutter, multi-object tracking in single or multiple views, analyzing motion patterns of large groups, and censusing wild animals in flight. Our data describe real world scenarios, such as bats emerging from their caves in large numbers, a crowded street view during a marathon competition, and students walking through an atrium during class break. We
more &raquo; ... so introduce baseline methods and evaluation protocols for these tasks. Our TIV benchmark enriches and diversifies video data sets available to the research community with thermal infrared footage, which poses new and challenging video analysis problems. We hope the TIV benchmark will help the community to better understand these interesting problems, generate new ideas, and value it as a testbed to compare solutions.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/cvprw.2014.39">doi:10.1109/cvprw.2014.39</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/cvpr/WuFTB14.html">dblp:conf/cvpr/WuFTB14</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/pllhsqdgnfg27psyhwh5pa44dy">fatcat:pllhsqdgnfg27psyhwh5pa44dy</a> </span>
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