基于第一人称办公室视频的隐私感知活动分类
Privacy-Aware Activity Classification from First Person Office Videos
摘要：
随着可穿戴人体摄像机的出现，第一人称视频（FPV）中的人类活动分类已经成为各种应用中越来越重要的主题，包括生活记录，执法，体育，工作场所和医疗保健。FPV的挑战性方面之一是其暴露于用户视野内的潜在敏感物体。在这项工作中，我们开发了一个隐私感知的活动分类系统，专注于办公室视频。我们利用具有Inception-ResNet混合的Mask-RCNN作为特征提取器来检测，然后模糊敏感对象（例如，数字屏幕，人脸，纸张）。对于活动分类，我们将递归神经网络（RNN）与基于ResNet，ResNext和DenseNet的特征提取器集成在一起。该系统在FPV办公视频数据集上进行了训练和评估，该数据集包括通过IEEE视频和图像处理（VIP）杯2019比赛提供的18个类别。在原始的无保护FPV上，所提出的活动分类器集成达到了85.078%的准确率，精确度，召回率和F1分数分别为0.88，0.85和0.86。在隐私保护视频上，性能略有下降，准确率，精确率，召回率和F1得分分别为73.68%，0.79，0.75和0.74。该系统在IEEE VIP Cup 2019竞赛中获得三等奖。
英文摘要：
In the advent of wearable body-cameras, human activity classification from First-Person Videos (FPV) has become a topic of increasing importance for various applications, including in life-logging, law-enforcement, sports, workplace, and healthcare. One of the challenging aspects of FPV is its exposure to potentially sensitive objects within the user's field of view. In this work, we developed a privacy-aware activity classification system focusing on office videos. We utilized a Mask-RCNN with an Inception-ResNet hybrid as a feature extractor for detecting, and then blurring out sensitive objects (e.g., digital screens, human face, paper) from the videos. For activity classification, we incorporate an ensemble of Recurrent Neural Networks (RNNs) with ResNet, ResNext, and DenseNet based feature extractors. The proposed system was trained and evaluated on the FPV office video dataset that includes 18-classes made available through the IEEE Video and Image Processing (VIP) Cup 2019 competition. On the original unprotected FPVs, the proposed activity classifier ensemble reached an accuracy of 85.078% with precision, recall, and F1 scores of 0.88, 0.85 & 0.86, respectively. On privacy protected videos, the performances were slightly degraded, with accuracy, precision, recall, and F1 scores at 73.68%, 0.79, 0.75, and 0.74, respectively. The presented system won the 3rd prize in the IEEE VIP Cup 2019 competition.
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。
