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An Improved Multi - domain Convolution Tracking Algorithm

机译：一种改进的多域卷积跟踪算法

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摘要

Along with the wide application of the Deep Learning in the field of Computer vision, Deep learning has become a mainstream direction in the field of object tracking. The tracking algorithm in this paper is based on the improved multi-domain convolution neural network, and the VOT video set is pre-trained on the network by multi-domain training strategy. In the process of online tracking, the network evaluates candidate targets sampled from vicinity of the prediction target in the previous with Gaussian distribution, and the candidate target with the highest score is recognized as the prediction target of this frame. The Bounding Box Regression model is introduced to make the prediction target closer to the ground-truths target box of the test set. Grouping-update strategy is involved to extract and select useful update samples in each frame, which can effectively prevent over fitting. And adapt to changes in both target and environment. To improve the speed of the algorithm while maintaining the performance, the number of candidate target succeed in adjusting dynamically with the help of Self-adaption parameter Strategy. Finally, the algorithm is tested by OTB set, compared with other high-performance tracking algorithms, and the plot of success rate and the accuracy are drawn, which illustrates outstanding performance of the tracking algorithm in this paper.
机译：随着深度学习在计算机视觉领域的广泛应用，深度学习已成为对象跟踪领域的主流方向。本文的跟踪算法基于改进的多域卷积神经网络，通过多域训练策略在网络上对VOT视频集进行了预训练。在在线跟踪的过程中，网络评估从先前具有高斯分布的预测目标附近采样的候选目标，并且将得分最高的候选目标识别为该帧的预测目标。引入边界框回归模型以使预测目标更接近测试集的真实目标框。分组更新策略涉及在每个帧中提取和选择有用的更新样本，这可以有效地防止过度拟合。并适应目标和环境的变化。为了在保持性能的同时提高算法的速度，借助自适应参数策略，可以成功地动态调整候选目标的数量。最后，将该算法与其他高性能跟踪算法进行了比较，并通过OTB集合进行了测试，绘制了成功率和准确性的曲线图，说明了该跟踪算法的出色性能。

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