Deep Neural Networks (DNNs) require large amounts of annotated training data
for a good performance. Often this data is generated using manual labeling
(error-prone and time-consuming) or rendering (requiring geometry and material
information). Both approaches make it difficult or uneconomic to apply them to
many small-scale applications. A fast and straightforward approach of acquiring
the necessary training data would allow the adoption of deep learning to even
the smallest of applications. Chroma keying is the process of replacing a color
(usually blue or green) with another background. Instead of chroma keying, we
propose luminance keying for fast and straightforward training image
acquisition. We deploy a black screen with high light absorption (99.99\%) to
record roughly 1-minute long videos of our target objects, circumventing
typical problems of chroma keying, such as color bleeding or color overlap
between background color and object color. Next we automatically mask our
objects using simple brightness thresholding, saving the need for manual
annotation. Finally, we automatically place the objects on random backgrounds
and train a 2D object detector. We do extensive evaluation of the performance
on the widely-used YCB-V object set and compare favourably to other
conventional techniques such as rendering, without needing 3D meshes, materials
or any other information of our target objects and in a fraction of the time
needed for other approaches. Our work demonstrates highly accurate training
data acquisition allowing to start training state-of-the-art networks within
minutes.

我们提出了一种快速和简单的训练图像获取方法，使用高吸收度（99.99％）的黑色屏幕录制目标对象的约 1 分钟长视频，通过简单的亮度阈值处理自动划分对象区域，将对象放置在随机背景上并训练 2D 目标检测器，避免了传统技术的 3D 网格、材料或其他信息的需求，极大地提高了训练数据获取的准确性和效率。

使用黑屏亮度键控进行目标检测和分割的快速训练数据获取

Fast Training Data Acquisition for Object Detection and Segmentation  using Black Screen Luminance Keying

Deep learning-based alpha matting showed tremendous improvements in recent
years, yet, feature film production studios still rely on classical chroma
keying including costly post-production steps. This perceived discrepancy can
be explained by some missing links necessary for production which are currently
not adequately addressed in the alpha matting community, in particular
foreground color estimation or color spill compensation. We propose a neural
network-based temporal multi-backdrop production system that combines
beneficial features from chroma keying and alpha matting. Given two consecutive
frames with different background colors, our one-encoder-dual-decoder network
predicts foreground colors and alpha values using a patch-based overlap-blend
approach. The system is able to handle imprecise backdrops, dynamic cameras,
and dynamic foregrounds and has no restrictions on foreground colors. We
compare our method to state-of-the-art algorithms using benchmark datasets and
a video sequence captured by a demonstrator setup. We verify that a dual
backdrop input is superior to the usually applied trimap-based approach. In
addition, the proposed studio set is actor friendly, and produces high-quality,
temporal consistent alpha and color estimations that include a superior color
spill compensation.

本文提出利用基于神经网络的时序多背景制作系统，结合了绿幕抠图和 alpha matting , 可以最大限度解决色散问题并保证高质量的拍摄效果。