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Visual tracking based on stacked Denoising Autoencoder network with genetic algorithm optimization

机译：基于堆叠降噪自动编码器网络遗传算法优化的视觉跟踪

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摘要

Visual object tracking in dynamic environments with severe appearance variations is a significant problem in the computer vision field. This paper proposes a novel visual tracking algorithm that exploits the multiple level features learning ability of SDAE. There are two training stages for the SDAE network: Layer-wise pre-training and fine-tuning. In the pre-training stage, a two-layer sparse-coded method is used to represent the input image, then a multi-level image feature descriptor is obtained. In the fine-tuning stage, the connection weights and bias terms for back propagation are gathered via genetic algorithm. A logistic classification layer is added at the top of the encoder network to enable tracking within the well-established particle filter network. Experimental results confirm, both qualitatively and quantitatively, that the proposed method performs well in comparison against eight other state-of-the-art methods.
机译：在动态环境中具有严重外观变化的视觉对象跟踪是计算机视觉领域中的重要问题。本文提出了一种新颖的视觉跟踪算法，该算法利用了SDAE的多层次特征学习能力。 SDAE网络有两个训练阶段：分层预训练和微调。在预训练阶段，使用两层稀疏编码方法来表示输入图像，然后获得多级图像特征描述符。在微调阶段，通过遗传算法收集反向传播的连接权重和偏差项。在编码器网络的顶部添加了逻辑分类层，以在完善的粒子过滤器网络内进行跟踪。实验结果从定性和定量两个方面证实，与其他八种最新技术相比，该方法表现良好。

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