论文标题
视频对象检测的经常性神经网络
Recurrent Neural Networks for video object detection
论文作者
论文摘要
图像中有很多有关对象检测的科学工作。对于许多应用程序，例如自动驾驶必须进行分类的实际数据是视频。这项工作比较了不同的方法，尤其是那些使用复发神经网络检测视频中对象的方法。我们在基于特征的方法之间有所不同，这些方法将不同框架的特征图馈入复发单元，盒子级方法，这些方法将带有类概率的边界框馈入使用流网络的复发单元和方法。这项研究表明了比较方法的共同结果，例如将时间上下文纳入对象检测中的好处，并指出视频对象检测网络的结论和指南。
There is lots of scientific work about object detection in images. For many applications like for example autonomous driving the actual data on which classification has to be done are videos. This work compares different methods, especially those which use Recurrent Neural Networks to detect objects in videos. We differ between feature-based methods, which feed feature maps of different frames into the recurrent units, box-level methods, which feed bounding boxes with class probabilities into the recurrent units and methods which use flow networks. This study indicates common outcomes of the compared methods like the benefit of including the temporal context into object detection and states conclusions and guidelines for video object detection networks.
