论文标题
改进的参与者关系图基于基于组的群体活动识别
Improved Actor Relation Graph based Group Activity Recognition
论文作者
论文摘要
视频理解是要识别和分类视频中出现的不同动作或活动。许多先前的工作，例如视频字幕，在产生一般视频理解方面表现出了令人鼓舞的表现。但是，使用最先进的视频字幕技巧来生成对人类行为及其相互作用的精细描述仍然具有挑战性。人类行动和小组活动的详细描述是必不可少的信息，可以在实时闭路电视视频监视，医疗保健，体育视频分析等中使用。本研究提出了一种视频理解方法，主要通过学习成对的演员外观相似性和参与者的位置来重点介绍小组活动识别。我们建议使用归一化的互相关（NCC）和绝对差异（SAD）的总和来计算配对的外观相似性并构建Actor关系图，以允许图形卷积网络学习如何对组活动进行分类。我们还建议将Mobilenet用作从每个视频框架中提取功能的骨干。进一步引入可视化模型，以可视化每个输入视频框架，并在每个人类对象上有预测的边界框，并预测个人动作和集体活动。
Video understanding is to recognize and classify different actions or activities appearing in the video. A lot of previous work, such as video captioning, has shown promising performance in producing general video understanding. However, it is still challenging to generate a fine-grained description of human actions and their interactions using state-of-the-art video captioning techniques. The detailed description of human actions and group activities is essential information, which can be used in real-time CCTV video surveillance, health care, sports video analysis, etc. This study proposes a video understanding method that mainly focused on group activity recognition by learning the pair-wise actor appearance similarity and actor positions. We propose to use Normalized cross-correlation (NCC) and the sum of absolute differences (SAD) to calculate the pair-wise appearance similarity and build the actor relationship graph to allow the graph convolution network to learn how to classify group activities. We also propose to use MobileNet as the backbone to extract features from each video frame. A visualization model is further introduced to visualize each input video frame with predicted bounding boxes on each human object and predict individual action and collective activity.
