Compared with object detection in static images, object detection in videos is more challenging due to degraded image qualities. An effective way to address this problem is to exploit temporal contexts by linking the same object across video to form tubelets and aggregating classification scores in the tubelets. In this paper, we focus on obtaining high quality object linking results for better classification. Unlike previous methods that link objects by checking boxes between neighboring frames, we propose to link in the same frame. To achieve this goal, we extend prior methods in following aspects: (1) a cuboid proposal network that extracts spatio-temporal candidate cuboids which bound the movement of objects; (2) a short tubelet detection network that detects short tubelets in short video segments; (3) a short tubelet linking algorithm that links temporally-overlapping short tubelets to form long tubelets. Experiments on the ImageNet VID dataset show that our method outperforms both the static image detector and the previous state of the art. In particular, our method improves results by 8.8 percent over the static image detector for fast moving objects.

中文翻译：

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通过高质量对象链接进行视频中的对象检测


与静态图像中的目标检测相比，视频中的目标检测由于图像质量下降而更具挑战性。解决这个问题的一个有效方法是利用时间上下文，将视频中的同一对象链接起来形成小管，并聚合小管中的分类分数。在本文中，我们专注于获得高质量的对象链接结果以实现更好的分类。与以前通过检查相邻帧之间的框来链接对象的方法不同，我们建议在同一帧中链接。为了实现这一目标，我们在以下方面扩展了现有方法：（1）长方体提议网络，提取限制对象运动的时空候选长方体； (2)短tubelet检测网络，检测短视频片段中的短tubelet； (3)短管连接算法，其将时间上重叠的短管连接成长管。在 ImageNet VID 数据集上的实验表明，我们的方法优于静态图像检测器和之前的技术水平。特别是，对于快速移动的物体，我们的方法比静态图像检测器的结果提高了 8.8%。