标题： 一种统一的方法用于建模和识别个体动作与群体活动 显示英文标题

标题： A Unified Approach for Modeling and Recognition of Individual Actions and Group Activities

摘要： 识别群体活动具有挑战性，因为难以隔离个体实体，找到个体各自扮演的角色以及表示参与者之间的复杂交互。 视频中的个体动作和群体活动可以在一个共同的框架中表示，因为它们具有以下共同特征：两者都由一组描述运动的低级特征组成，例如每个像素的光流或每个特征点的轨迹，根据时间和空间维度的一组组合约束。 在本文中，我们提出一个统一的模型来评估两个给定的个体或群体活动之间的相似性。 我们的方法避免了显式提取个体演员，识别和表示人与人之间的交互。 通过所提出的方法，可以从视频数据库中进行查询-by-示例检索；并通过查询包含已知活动的视频来识别活动。 建议的视频匹配过程可以以无监督的方式进行。 我们通过识别一组人类动作和足球战术来展示我们方法的性能。 显示英文摘要

摘要： Recognizing group activities is challenging due to the difficulties in isolating individual entities, finding the respective roles played by the individuals and representing the complex interactions among the participants. Individual actions and group activities in videos can be represented in a common framework as they share the following common feature: both are composed of a set of low-level features describing motions, e.g., optical flow for each pixel or a trajectory for each feature point, according to a set of composition constraints in both temporal and spatial dimensions. In this paper, we present a unified model to assess the similarity between two given individual or group activities. Our approach avoids explicit extraction of individual actors, identifying and representing the inter-person interactions. With the proposed approach, retrieval from a video database can be performed through Query-by-Example; and activities can be recognized by querying videos containing known activities. The suggested video matching process can be performed in an unsupervised manner. We demonstrate the performance of our approach by recognizing a set of human actions and football plays.