SHOP：一个基于深度学习的管道，用于近实时的检测模糊视频中存在的小型手持物体
SHOP: A Deep Learning Based Pipeline for near Real-Time Detection of Small Handheld Objects Present in Blurry Video
摘要：
虽然先前的工作已经研究和开发了能够进行目标检测的计算模型，但是模型仍然难以可靠地解释具有运动模糊和小目标的图像。此外，这些型号都不是专门为手持式目标检测设计的。在这项工作中，我们提出了SHOP（小型手持对象管道），一个管道，可靠和有效地解释模糊的图像包含手持对象。管道每个阶段使用的特定模型是灵活的，可以根据性能要求进行更改。首先，图像被去模糊，然后通过姿势检测系统，其中在任何在场的人的手周围提出感兴趣的区域。接下来，通过单级对象检测器对图像执行目标检测。最后，所提出的感兴趣区域被用来过滤掉低置信度的检测。在Microsoft Common Objects in Context（MS COCO）的手持设备子集上进行的测试表明，这三个阶段的过程导致误报减少了70%，而在其最强配置中，误报仅减少了17%。我们还提出了一个子集的MS COCO组成的手持对象，可用于继续发展的手持目标检测方法。https://github.com/spider-sense/SHOP
英文摘要：
While prior works have investigated and developed computational models capable of object detection, models still struggle to reliably interpret images with motion blur and small objects. Moreover, none of these models are specifically designed for handheld object detection. In this work, we present SHOP (Small Handheld Object Pipeline), a pipeline that reliably and efficiently interprets blurry images containing handheld objects. The specific models used in each stage of the pipeline are flexible and can be changed based on performance requirements. First, images are deblurred and then run through a pose detection system where areas-of-interest are proposed around the hands of any people present. Next, object detection is performed on the images by a single-stage object detector. Finally, the proposed areas-of-interest are used to filter out low confidence detections. Testing on a handheld subset of Microsoft Common Objects in Context (MS COCO) demonstrates that this 3 stage process results in a 70 percent decrease in false positives while only reducing true positives by 17 percent in its strongest configuration. We also present a subset of MS COCO consisting solely of handheld objects that can be used to continue the development of handheld object detection methods. https://github.com/spider-sense/SHOP
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
