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While prior works have investigated and developed computational modelscapable of object detection, models still struggle to reliably interpret imageswith motion blur and small objects. Moreover, none of these models arespecifically designed for handheld object detection. In this work, we presentSHOP (Small Handheld Object Pipeline), a pipeline that reliably and efficientlyinterprets blurry images containing handheld objects. The specific models usedin each stage of the pipeline are flexible and can be changed based onperformance requirements. First, images are deblurred and then run through apose detection system where areas-of-interest are proposed around the hands ofany people present. Next, object detection is performed on the images by asingle-stage object detector. Finally, the proposed areas-of-interest are usedto filter out low confidence detections. Testing on a handheld subset ofMicrosoft Common Objects in Context (MS COCO) demonstrates that this 3 stageprocess results in a 70 percent decrease in false positives while only reducingtrue positives by 17 percent in its strongest configuration. We also present asubset of MS COCO consisting solely of handheld objects that can be used tocontinue the development of handheld object detection methods.https://github.com/spider-sense/SHOP

SHOP: A Deep Learning Based Pipeline for Near Real-Time Detection of Small Handheld Objects Present in Blurry Video