Computer vision is used in this work to detect lighting elements in buildings
with the goal of improving the accuracy of previous methods to provide a
precise inventory of the location and state of lamps. Using the framework
developed in our previous works, we introduce two new modifications to enhance
the system: first, a constraint on the orientation of the detected poses in the
optimization methods for both the initial and the refined estimates based on
the geometric information of the building information modelling (BIM) model;
second, an additional reprojection error filtering step to discard the
erroneous poses introduced with the orientation restrictions, keeping the
identification and localization errors low while greatly increasing the number
of detections. These~enhancements are tested in five different case studies
with more than 30,000 images, with results showing improvements in the number
of detections, the percentage of correct model and state identifications, and
the distance between detections and reference positions

利用计算机视觉方法检测建筑物中的照明元件，通过引入两项改进措施提高系统精确性：首先，在建筑信息模型的几何信息基础上对检测姿态的初始估计和改进估计进行优化方法中引入方向约束，其次，在引入方向约束后通过附加的重新投影误差过滤步骤来排除错误姿态，从而在保持定位和识别误差较低的同时大大增加检测数量。通过五个不同案例研究使用超过 30,000 张图片进行测试，结果显示在检测数量、正确模型和状态识别的比例以及检测与参考位置之间的距离方面均有所改善。

基于计算机视觉的建筑物灯具检测的方向受限系统

Orientation-Constrained System for Lamp Detection in Buildings Based on  Computer Vision

Up to 150000 asteroids will be visible in the images of the ESA Euclid space
telescope, and the instruments of Euclid offer multiband visual to
near-infrared photometry and slitless spectra of these objects. Most asteroids
will appear as streaks in the images. Due to the large number of images and
asteroids, automated detection methods are needed. A non-machine-learning
approach based on the StreakDet software was previously tested, but the results
were not optimal for short and/or faint streaks. We set out to improve the
capability to detect asteroid streaks in Euclid images by using deep learning.
We built, trained, and tested a three-step machine-learning pipeline with
simulated Euclid images. First, a convolutional neural network (CNN) detected
streaks and their coordinates in full images, aiming to maximize the
completeness (recall) of detections. Then, a recurrent neural network (RNN)
merged snippets of long streaks detected in several parts by the CNN. Lastly,
gradient-boosted trees (XGBoost) linked detected streaks between different
Euclid exposures to reduce the number of false positives and improve the purity
(precision) of the sample.
The deep-learning pipeline surpasses the completeness and reaches a similar
level of purity of a non-machine-learning pipeline based on the StreakDet
software. Additionally, the deep-learning pipeline can detect asteroids
0.25-0.5 magnitudes fainter than StreakDet. The deep-learning pipeline could
result in a 50% increase in the number of detected asteroids compared to the
StreakDet software. There is still scope for further refinement, particularly
in improving the accuracy of streak coordinates and enhancing the completeness
of the final stage of the pipeline, which involves linking detections across
multiple exposures.

通过使用深度学习方法，我们建立、训练和测试了一种机器学习流程，可以提高在 Euclid 图像中检测小行星痕迹的能力，大大增加了检测到的小行星数量。

Euclid：使用深度学习在模拟图像中识别小行星轨迹

Euclid: Identification of asteroid streaks in simulated images using  deep learning

The main challenge of online multi-object tracking is to reliably associate
object trajectories with detections in each video frame based on their tracking
history. In this work, we propose the Recurrent Autoregressive Network (RAN), a
temporal generative modeling framework to characterize the appearance and
motion dynamics of multiple objects over time. The RAN couples an external
memory and an internal memory. The external memory explicitly stores previous
inputs of each trajectory in a time window, while the internal memory learns to
summarize long-term tracking history and associate detections by processing the
external memory. We conduct experiments on the MOT 2015 and 2016 datasets to
demonstrate the robustness of our tracking method in highly crowded and
occluded scenes. Our method achieves top-ranked results on the two benchmarks.

本文提出了一种循环自回归网络（RAN），它是一种时间生成建模框架，可以可靠地将物体轨迹与每个视频帧中的检测相关联，并通过内、外存储器来处理多个物体的外貌和运动动态。实验证明，我们的追踪方法在高度拥挤和遮挡的场景中具有鲁棒性，并在 MOT2015 和 2016 数据集上取得了排名最高的结果。