Motion information can be important for detecting objects, but it has been used less for pedestrian detection, particularly with deep-learning-based methods. We propose a method that uses deep motion features as well as deep still-image features, following the success of two-stream convolutional networks, each of which are trained separately for spatial and temporal streams. To extract motion clues for detection differentiated from other background motions, the temporal stream takes as input the difference in frames that are weakly stabilized by optical flow. To make the networks applicable to bounding-box-level detection, the mid-level features are concatenated and combined with a sliding-window detector. We also introduce transfer learning from multiple sources in the two-stream networks, which can transfer still image and motion features from ImageNet and an action recognition dataset respectively, to overcome the insufficiency of training data for convolutional neural networks in pedestrian datasets. We conducted an evaluation on two popular large-scale pedestrian benchmarks, namely the Caltech Pedestrian Detection Benchmark and Daimler Mono Pedestrian Detection Benchmark. We observed 10% improvement compared to the same method but without motion features.

中文翻译：

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通过两流ConvNets具有运动功能的行人检测

运动信息对于检测物体可能很重要，但对于行人检测却很少使用，尤其是在基于深度学习的方法中。在两流卷积网络取得成功之后，我们提出了一种使用深运动特征以及深静止图像特征的方法，其中每一种分别针对空间和时间流进行训练。为了提取区别于其他背景运动的运动线索以进行检测，时间流将由光流弱稳定的帧中的差异作为输入。为了使网络适用于边界框级别的检测，将中级功能串联并与滑动窗口检测器组合。我们还将介绍两流网络中来自多个来源的转移学习，它可以分别从ImageNet和动作识别数据集中传输静止图像和运动特征，以克服行人数据集中卷积神经网络训练数据的不足。我们对两种流行的大型行人基准进行了评估，即Caltech行人检测基准和戴姆勒单行人检测基准。与没有运动功能的相同方法相比，我们观察到了10％的改善。