We are concerned about retrieving a query person from the videos taken by a non-overlapping camera network. Existing methods often rely on pure visual matching or consider temporal constraint, but ignore the spatial information of the camera network. To address this problem, we propose a framework of person retrieval based on cross-camera trajectory generation which integrates both temporal and spatial information. To obtain the pedestrian trajectories, we propose a new cross-camera spatio-temporal model that integrates the walking habits of pedestrians and the path layout between cameras, forming a joint probability distribution. Such a spatio-temporal model among a camera network can be specified using sparsely sampled pedestrian data. Based on the spatio-temporal model, the cross-camera trajectories of a specific pedestrian can be extracted by the conditional random field model, and further optimized by the restricted nonnegative matrix factorization. Finally, a trajectory re-ranking technology is proposed to improve the person retrieval results. To verify the effectiveness of our approach, we build the first dataset of cross-camera pedestrian trajectories over an actual monitoring scenario, namely the Person Trajectory Dataset. Extensive experiments have verified the effectiveness and robustness of the proposed method.

本文提出了一种基于跨摄像头轨迹生成的行人检索框架，该框架整合了时间和空间信息，并通过建立稀疏采样行人数据来构建跨摄像头的空间-时间模型，进而提取出交叉摄像头轨迹，并结合条件随机场模型和非负矩阵分解方法进行优化， 最终通过轨迹重新排序技术来提高检索结果。为了验证方法的有效性，本文构建了第一个跨摄像头行人轨迹数据集，即人员轨迹数据集，并进行了广泛实验。

跨摄像头轨迹协助摄像头网络中的人员检索