论文标题
在压缩域中的实时在线多目标跟踪
Real-time Online Multi-Object Tracking in Compressed Domain
论文作者
论文摘要
最近的在线多对象跟踪（MOT）方法已经达到了理想的跟踪性能。但是，大多数现有方法的跟踪速度相当慢。灵感来自相邻帧高度相关和冗余的事实，我们分别将框架分为密钥和非钥匙框架，并在压缩域中跟踪对象。对于关键帧，RGB图像将恢复以进行检测和数据关联。为了使数据关联更可靠，提出了可以与检测器共同训练的外观卷积神经网络（CNN）。对于非钥匙帧，基于压缩域中提供的运动信息，对象是由跟踪CNN直接传播的。与最先进的在线MOT方法相比，我们的跟踪器在保持可比的跟踪性能的同时，我们的跟踪器的速度约为6倍。
Recent online Multi-Object Tracking (MOT) methods have achieved desirable tracking performance. However, the tracking speed of most existing methods is rather slow. Inspired from the fact that the adjacent frames are highly relevant and redundant, we divide the frames into key and non-key frames respectively and track objects in the compressed domain. For the key frames, the RGB images are restored for detection and data association. To make data association more reliable, an appearance Convolutional Neural Network (CNN) which can be jointly trained with the detector is proposed. For the non-key frames, the objects are directly propagated by a tracking CNN based on the motion information provided in the compressed domain. Compared with the state-of-the-art online MOT methods,our tracker is about 6x faster while maintaining a comparable tracking performance.
