论文标题
商店：基于深度学习的管道，用于近实时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
论文作者
论文摘要
尽管先前的工作已经调查并开发了能够对象检测的计算模型，但模型仍然难以用运动模糊和小物体来可靠地解释图像。此外，这些模型均未专门设计用于手持对象检测。在这项工作中，我们展示了商店（小型手持式物体管道），该管道可靠，有效地解释包含手持物体的模糊图像。管道的每个阶段中使用的特定模型都是灵活的，并且可以根据性能要求更改。首先，图像被脱毛，然后穿过姿势检测系统，在那里，在那里的任何人的手上都提出了利益区域。接下来，通过单阶段对象检测器在图像上执行对象检测。最后，提出的利益领域用于滤除低置信度检测。在上下文中Microsoft公共对象的手持式子集（MS COCO）上进行测试表明，这三阶段过程导致假阳性的70％降低，而仅在其最强的配置中将真实阳性降低了17％。我们还介绍了MS Coco的子集，该子集仅由手持对象组成，这些物体可用于继续开发手持对象检测方法。 https://github.com/spider-sense/shop
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
