We are concerned with retrieving a query person from multiple videos captured by a non-overlapping camera network. Existing methods often rely on purely visual matching or consider temporal constraints but ignore the spatial information of the camera network. To address this issue, we propose a pedestrian retrieval framework based on cross-camera trajectory generation, which integrates both temporal and spatial information. To obtain pedestrian trajectories, we propose a novel cross-camera spatio-temporal model that integrates pedestrians' walking habits and the path layout between cameras to form 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, cross-camera trajectories can be extracted by the conditional random field model and further optimized by restricted non-negative matrix factorization. Finally, a trajectory re-ranking technique is proposed to improve the pedestrian retrieval results. To verify the effectiveness of our method, we construct the first cross-camera pedestrian trajectory dataset, the Person Trajectory Dataset, in real surveillance scenarios. Extensive experiments verify the effectiveness and robustness of the proposed method.

翻译：我们关注于从非重叠摄像机网络所拍摄的多段视频中检索目标行人。现有方法通常依赖于纯视觉匹配或考虑时间约束，但忽略了摄像机网络的空间信息。为解决这一问题，我们提出了一种基于跨摄像头轨迹生成的行人检索框架，该框架融合了时间与空间信息。为获取行人轨迹，我们提出了一种新颖的跨摄像头时空模型，该模型整合了行人的行走习惯与摄像机间的路径布局，形成联合概率分布。这种摄像机网络间的时空模型可通过稀疏采样的行人数据进行具体化。基于该时空模型，利用条件随机场模型可提取跨摄像头轨迹，并通过受限非负矩阵分解进一步优化。最后，提出轨迹重排序技术以改进行人检索结果。为验证方法有效性，我们在真实监控场景中构建了首个跨摄像头行人轨迹数据集——Person Trajectory Dataset。大量实验证明了该方法的有效性和鲁棒性。