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首页> 外文期刊>Journal of visual communication & image representation >Generalized multiple sparse information fusion for vehicle re-identification
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Generalized multiple sparse information fusion for vehicle re-identification

机译：用于车辆重新识别的广义多稀疏信息融合

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摘要

Vehicle re-identification (reID) aims to search the target vehicle in a non-overlapping multi-camera network, which is important for the intelligent analysis in large scale of surveillance videos. Many existing methods have employed various techniques to achieve discriminative information. However, those methods always focus on the description of one view for the same vehicle images. Hence, a generated multiple sparse information fusion method is proposed for exploiting latent features from multi-views, which employs three different deep networks to extract multiple features from coarse to fine. And these features are regarded as multi-view features. Besides, to fuse these features reasonably, the paper transfers various features into a common space for better seeking distinctive features. Especially, besides ResNet, two feature learning networks are proposed to learn different features, respectively. One is designed to learn robust feature by dropping some features randomly when training the reID model. Another is to combine various salient features from different layers, which forms strong features for the reID task. Moreover, comprehensive experimental results have demonstrated that our proposed method can achieve competitive performances on benchmark datasets VehicleID and VeRi-776.
机译：车辆重新识别（Reid）旨在在非重叠的多相机网络中搜索目标车辆，这对于大规模监视视频中的智能分析非常重要。许多现有方法采用了各种技术来实现歧视信息。然而，这些方法始终关注同一车辆图像的一个视图的描述。因此，提出了一种生成的多稀疏信息融合方法，用于利用多视图的潜在特征，该多视图采用三个不同的深网络来从粗略到精细提取多个特征。这些功能被视为多视图功能。此外，为了合理地熔化这些功能，纸张将各种特征传送到共同的空间中，以便更好地寻求独特的特征。特别是，除Reset之外，提出了两个特征学习网络以分别学习不同的特征。旨在通过在培训Reid模型时随机丢弃一些功能来学习强大的功能。另一个是将来自不同层的各种突出特征组合，这构成了Reid任务的强特征。此外，全面的实验结果表明，我们的提出方法可以在基准数据集和Veri-776上实现竞争性表演。

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