Vehicle type recognition (VTR) from surveillance data has recently received increasing attention in both intelligent transportation and computer vision field. The deep learning techniques, e.g., convolutional neural networks (CNNs), have provided great progress in tasks with the large-scale labeled data. However, in real-world applications, manually labeling from large-scale surveillance data is time-consuming and tedious, which strongly obstructs the application of a CNN-based VTR. The existing works on VTR studied samples selection for manual annotation. Here, a VTR framework based on deep active learning that releases the burden of large-scale labeling is presented. The framework selects the most worthy samples to the manually selected, and then retrains the network with the annotated samples incrementally. Besides, the framework simultaneously takes advantage of both auto-labeled and manually-labeled data using the strategy of bi-direction entropy threshold. The proposed framework is validated on the public dataset Comprehensive Cars, and experimental results demonstrated that, with the incremental query strategy, the proposed framework could reduce annotation cost dramatically compared with random selection.

中文翻译：

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基于深度主动学习的监控数据车型识别


来自监控数据的车辆类型识别（VTR）最近在智能交通和计算机视觉领域受到越来越多的关注。深度学习技术，例如卷积神经网络（CNN），在处理大规模标记数据的任务中取得了巨大进展。然而，在实际应用中，对大规模监控数据进行手动标记既耗时又繁琐，这严重阻碍了基于 CNN 的 VTR 的应用。 VTR 的现有工作研究了手动注释的样本选择。这里提出了一种基于深度主动学习的VTR框架，可以减轻大规模标注的负担。该框架选择最有价值的样本进行手动选择，然后使用带注释的样本增量重新训练网络。此外，该框架使用双向熵阈值策略同时利用自动标记和手动标记的数据。所提出的框架在公共数据集Comfortable Cars上进行了验证，实验结果表明，通过增量查询策略，所提出的框架与随机选择相比可以显着降低注释成本。