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Online Multiple Object Tracking Based on Open-Set Few-Shot Learning
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DOI：10.1109/ACCESS.2020.3032252.png)
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摘要
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How to make an online tracking model effectively adapt to newly appearing objects and object disappearance as well as appearance variations of target objects from few examples is an essential issue in multiple object tracking (MOT). Learning target appearances from few examples is a few-shot classification problem, while identifications of newly appearing objects and object disappearance has the aspect of open-set classification. In this work, we regard online MOT as open-set few-show classification to address both learning from few examples (few-shot classification) and unknown classes such as new objects (open-set classification). Specifically, we develop an embedding neural network, called VOFNet, consisting of convolutional and recurrent parts, to perform open-set few-shot classification. The convolutional part constructs a feature from an example of a target object and the recurrent part determines a representative feature of a target object from few examples. Then VOFNet is trained to provide effective features for open-set few-shot classification. Finally, we develop an online multiple object tracker based on the combination of VOFNet and the bipartite matching. The proposed tracker achieves 49.2 multiple object tracking accuracy (MOTA) with 28.9 frames per second on MOT17 dataset, which shows a significantly better trade-off between the accuracy and the speed than the existing algorithms. For example, the proposed algorithm yields about 3.17 times faster speed with 0.99 times lower accuracy than recent existing MOT algorithm [1].
Keyword:
Multiple object tracking
online tracking
open-set classification
few-shot classification
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期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
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引用论文
Multiple Object Tracking With Attention to Appearance, Structure, Motion and Size多目标跟踪，注意外观、结构、运动和大小.png)
IEEE ACCESSOAAI
IEEE ACCESSIF3.6
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