摄像机网络中人检索的语境约束

M. Bäuml, Makarand Tapaswi, Arne Schumann, R. Stiefelhagen
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引用次数: 2

摘要

我们在摄像机网络中使用上下文约束进行人物检索。我们首先对摄像机网络中人物轨迹的身份制定了一组一般的正面和负面约束,例如一个人不能在同一帧中出现两次。然后,我们将展示如何使用这些约束来改进人员检索。首先,我们使用约束以无监督的方式获得训练数据,以学习比欧几里得距离更适合于区分不同人的一般度量。其次,从初始查询轨迹开始,我们使用约束来增强查询集,以获得查询的额外正样本和负样本。第三,我们将人的检索任务定义为能量最小化问题,将轨迹分数和约束整合到一个共同的框架中,对所有相互关联的轨迹进行联合优化检索。我们在CAVIAR数据集上评估了我们的方法,在平均精度方面,与独立处理每个轨道的标准检索相比,我们的性能提高了22%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Contextual Constraints for Person Retrieval in Camera Networks
We use contextual constraints for person retrieval in camera networks. We start by formulating a set of general positive and negative constraints on the identities of person tracks in camera networks, such as a person cannot appear twice in the same frame. We then show how these constraints can be used to improve person retrieval. First, we use the constraints to obtain training data in an unsupervised way to learn a general metric that is better suited to discriminate between different people than the Euclidean distance. Second, starting from an initial query track, we enhance the query-set using the constraints to obtain additional positive and negative samples for the query. Third, we formulate the person retrieval task as an energy minimization problem, integrate track scores and constraints in a common framework and jointly optimize the retrieval over all interconnected tracks. We evaluate our approach on the CAVIAR dataset and achieve 22% relative performance improvement in terms of mean average precision over standard retrieval where each track is treated independently.
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