期刊
IEEE TRANSACTIONS ON MULTIMEDIA
卷 22, 期 3, 页码 666-675出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TMM.2019.2932615
关键词
Thermal infrared; pedestrian tracking; benchmark; dataset
资金
- National Natural Science Foundation of China [61672183, 61502119]
- Shenzhen Research Council [JCYJ20170815113552036, JCYJ20170413104556946, JCYJ20160406161948211, JCY-J2016022620 1453085]
- Natural Science Foundation of Guangdong Province [2015A030313544]
Thermal infrared (TIR) pedestrian tracking is one of the important components among numerous applications of computer vision, which has a major advantage: it can track pedestrians in total darkness. The ability to evaluate the TIR pedestrian tracker fairly, on a benchmark dataset, is significant for the development of this field. However, there is not a benchmark dataset. In this paper, we develop a TIR pedestrian tracking dataset for the TIR pedestrian tracker evaluation. The dataset includes 60 thermal sequences with manual annotations. Each sequence has nine attribute labels for the attribute based evaluation. In addition to the dataset, we carry out the large-scale evaluation experiments on our benchmark dataset using nine publicly available trackers. The experimental results help us understand the strengths and weaknesses of these trackers. In addition, in order to gain more insight into the TIR pedestrian tracker, we divide its functions into three components: feature extractor, motion model, and observation model. Then, we conduct three comparison experiments on our benchmark dataset to validate how each component affects the tracker's performance. The findings of these experiments provide some guidelines for future research.
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