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首页> 外文会议>International conference on graphic and image processing >Robust Visual Tracking via Multiple Discriminative Models with Object Proposals
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Robust Visual Tracking via Multiple Discriminative Models with Object Proposals

机译：通过带有对象提议的多种区分模型进行可靠的视觉跟踪

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摘要

Model drift is an important reason for tracking failure. In this paper, multiple discriminative models with object proposals are used to improve the model discrimination for relieving this problem. Firstly, the target location and scale changing are captured by lots of high-quality object proposals, which are represented by deep convolutional features for target semantics. And then, through sharing a feature map obtained by a pre-trained network, ROI pooling is exploited to wrap the various sizes of object proposals into vectors of the same length, which are used to learn a discriminative model conveniently. Lastly, these historical snapshot vectors are trained by different lifetime models. Based on entropy decision mechanism, the bad model owing to model drift can be corrected by selecting the best discriminative model. This would improve the robustness of the tracker significantly. We extensively evaluate our tracker on two popular benchmarks, the OTB 2013 benchmark and UAV20L benchmark. On both benchmarks, our tracker achieves the best performance on precision and success rate compared with the state-of-the-art trackers.
机译：模型漂移是跟踪失败的重要原因。在本文中，使用带有对象提议的多个判别模型来改善模型判别力，以缓解该问题。首先，目标位置和尺度的变化被许多高质量的目标提议所捕获，这些目标提议以目标语义的深层卷积特征为代表。然后，通过共享经过预训练的网络获得的特征图，利用ROI池将各种尺寸的对象建议书包装到相同长度的向量中，从而方便地学习判别模型。最后，这些历史快照向量由不同的生存期模型训练。基于熵决策机制，可以通过选择最佳判别模型来纠正由于模型漂移引起的不良模型。这将大大提高跟踪器的鲁棒性。我们在两种流行的基准（OTB 2013基准和UAV20L基准）上对跟踪器进行了广泛的评估。与最新的跟踪器相比，在两个基准上，我们的跟踪器在精度和成功率上均达到最佳性能。

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