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Efficient object-based surveillance image search using spatial pooling of convolutional features

机译：使用卷积特征的空间池进行基于对象的有效监视图像搜索

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Modern surveillance networks are large collections of computational sensor nodes, where each node can be programmed to capture, prioritize, segment salient objects, and transmit them to central repositories for indexing. Visual data from such networks grow exponentially and present many challenges concerning their transmission, storage, and retrieval. Searching for particular surveillance objects is a common but challenging task. In this paper, we present an efficient features extraction framework which utilizes an optimal subset of kernels from the first layer of a convolutional neural network pre-trained on ImageNet dataset for object-based surveillance image search. The input image is convolved with the set of kernels to generate feature maps, which are aggregated into a single feature map using a novel spatial maximal activator pooling approach. A low-dimensional feature vector is computed to represent surveillance objects. The proposed system provides improvements in both performance and efficiency over other similar approaches for surveillance datasets. (C) 2017 Published by Elsevier Inc.
机译：现代监视网络是计算传感器节点的大集合，每个节点都可以进行编程以捕获，区分优先级，分割显着对象并将其传输到中央存储库以进行索引。来自此类网络的可视数据呈指数增长，并在其传输，存储和检索方面提出了许多挑战。搜索特定的监视对象是一项常见但具有挑战性的任务。在本文中，我们提出了一种有效的特征提取框架，该框架利用ImageNet数据集上预训练的卷积神经网络第一层内核的最佳子集进行基于对象的监视图像搜索。将输入图像与一组内核进行卷积以生成特征图，然后使用一种新颖的空间最大激活器合并方法将这些特征图聚合为单个特征图。计算低维特征向量以表示监视对象。提出的系统相对于监视数据集的其他类似方法，在性能和效率上均提供了改进。 （C）2017由Elsevier Inc.发布

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