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Research on Target Tracking Algorithm Based on Siamese Neural Network

机译：基于暹罗神经网络的目标跟踪算法研究

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Target tracking is a significant topic in the field of computer vision. In this paper, the target tracking algorithm based on deep Siamese network is studied. Aiming at the situation that the tracking process is not robust, such as drift or miss the target, the tracking accuracy and robustness of the algorithm are improved by improving the feature extraction part and online update part. This paper adds SE-block and temporal attention mechanism (TAM) to the framework of Siamese neural network. SE-block can refine and extract features; different channels are given different weights according to their importance which can improve the discrimination of the network and the recognition ability of the tracker. Temporal attention mechanism can update the target state by adjusting the weights of samples at current frame and historical frame to solve the model drift caused by the existence of similar background. We use cross-entropy loss to distinguish the targets in different sequences so that their distance in the feature domains is longer and the features are easier to identify. We train and test the network on three benchmarks and compare with several state-of-the-art tracking methods. The experimental results demonstrate that the algorithm proposed is superior to other methods in tracking effect diagram and evaluation criteria. The proposed algorithm can solve the occlusion problem effectively while ensuring the real-time performance in the process of tracking.
机译：目标跟踪是计算机视野领域的重要主题。本文研究了基于深暹罗网络的目标跟踪算法。针对跟踪过程不是强大的情况，例如漂移或错过目标，通过改进特征提取部分和在线更新部分来提高算法的跟踪精度和鲁棒性。本文将SE-Block和TAM）添加到暹罗神经网络的框架上。 SE-Block可以细化和提取特征;根据其重要性给出不同的频道，这可以改善网络的辨别和跟踪器的识别能力。时间关注机制可以通过调整当前帧和历史框架的样本的权重来更新目标状态，以解决由类似背景引起的模型漂移。我们使用跨熵丢失来区分不同序列中的目标，使其在特征域中的距离更长，并且该功能更容易识别。我们在三个基准测试中培训并测试网络，并与几种最先进的跟踪方法进行比较。实验结果表明，所提出的算法优于跟踪效果图和评估标准的其他方法。该算法可以有效地解决遮挡问题，同时确保跟踪过程中的实时性能。

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