Annotating object ground truth in videos is vital for several downstream tasks in robot perception and machine learning, such as for evaluating the performance of an object tracker or training an image-based object detector. The accuracy of the annotated instances of the moving objects on every image frame in a video is crucially important. Achieving that through manual annotations is not only very time consuming and labor intensive, but is also prone to high error rate. State-of-the-art annotation methods depend on manually initializing the object bounding boxes only in the first frame and then use classical tracking methods, e.g., adaboost, or kernelized correlation filters, to keep track of those bounding boxes. These can quickly drift, thereby requiring tedious manual supervision. In this paper, we propose a new annotation method which leverages a combination of a learning-based detector (SSD) and a learning-based tracker (RE$^3$). Through this, we significantly reduce annotation drifts, and, consequently, the required manual supervision. We validate our approach through annotation experiments using our proposed annotation method and existing baselines on a set of drone video frames. Source code and detailed information on how to run the annotation program can be found at https://github.com/robot-perception-group/smarter-labelme

翻译：在视频中标注物体真实标注信息对于机器人感知和机器学习中的若干下游任务至关重要，例如评估目标跟踪器性能或训练基于图像的物体检测器。视频中每帧图像内运动物体标注实例的准确性至关重要。通过人工标注实现这一目标不仅耗时费力，而且容易产生高错误率。现有最先进的标注方法仅依赖在首帧中手动初始化目标边界框，随后采用经典跟踪方法（如AdaBoost或核化相关滤波器）对这些边界框进行持续追踪。这些方法容易快速发生漂移，从而需要繁琐的人工监督。本文提出一种新型标注方法，该方法融合了基于学习的检测器（SSD）与基于学习的跟踪器（RE³）。通过这种方法，我们显著减少了标注漂移，进而降低了所需的人工监督强度。我们通过在一组无人机视频帧上进行的标注实验，验证了所提方法相较于现有基线方法的有效性。源代码及如何运行标注程序的详细信息可参见https://github.com/robot-perception-group/smarter-labelme。