论文标题
基于学习的跟踪和基于单眼相机目标的对象检测的融合以下
Blending of Learning-based Tracking and Object Detection for Monocular Camera-based Target Following
论文作者
论文摘要
最近，深度学习已开始应用于视频流中通用对象的视觉跟踪。出于机器人应用的目的，对于目标跟踪器而言，如果由于重量或长时间的闭塞或运动模糊而丢失了目标，则非常重要。我们提出了一种实时方法，该方法将通用目标跟踪器和对象检测模块与目标重新识别模块融合在一起。我们的工作着重于在感兴趣的对象属于\ emph {熟悉}对象类别的情况下，改善卷积复发性神经网络的对象跟踪器的性能。我们提出的方法非常轻巧，可以在85-90 fps的同时以挑战性的基准获得竞争成果。
Deep learning has recently started being applied to visual tracking of generic objects in video streams. For the purposes of robotics applications, it is very important for a target tracker to recover its track if it is lost due to heavy or prolonged occlusions or motion blur of the target. We present a real-time approach which fuses a generic target tracker and object detection module with a target re-identification module. Our work focuses on improving the performance of Convolutional Recurrent Neural Network-based object trackers in cases where the object of interest belongs to the category of \emph{familiar} objects. Our proposed approach is sufficiently lightweight to track objects at 85-90 FPS while attaining competitive results on challenging benchmarks.
