论文标题
FasterVideo：有效的在线联合对象检测和跟踪
FasterVideo: Efficient Online Joint Object Detection And Tracking
论文作者
论文摘要
视频中的对象检测和跟踪代表了当前和将来的视觉感知系统的基本和计算要求的构建块。为了减少现实应用程序的可用方法和计算要求之间的效率差距，我们建议重新考虑图像对象检测最成功的方法之一，更快的R-CNN，并将其扩展到视频域。具体而言，我们将检测框架扩展到学习实例级嵌入的嵌入，这对数据关联和重新识别目的有益。为了关注检测和跟踪的计算方面，我们提出的方法达到了相关应用所需的非常高的计算效率，同时仍设法与最新和最新的方法竞争，如我们在标准对象跟踪基准测试基准上进行的实验所示
Object detection and tracking in videos represent essential and computationally demanding building blocks for current and future visual perception systems. In order to reduce the efficiency gap between available methods and computational requirements of real-world applications, we propose to re-think one of the most successful methods for image object detection, Faster R-CNN, and extend it to the video domain. Specifically, we extend the detection framework to learn instance-level embeddings which prove beneficial for data association and re-identification purposes. Focusing on the computational aspects of detection and tracking, our proposed method reaches a very high computational efficiency necessary for relevant applications, while still managing to compete with recent and state-of-the-art methods as shown in the experiments we conduct on standard object tracking benchmarks
