IEEE Trans Image Process. 2023;32:3806-3820. doi: 10.1109/TIP.2023.3290515. Epub 2023 Jul 12.
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 that 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 cross-camera spatio-temporal model 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 optimised 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.
我们关注的是从由非重叠摄像机网络捕获的多个视频中检索查询对象。现有的方法通常依赖于纯粹的视觉匹配，或者考虑时间约束，但忽略了摄像机网络的空间信息。为了解决这个问题，我们提出了一种基于跨摄像机轨迹生成的行人检索框架，该框架集成了时间和空间信息。为了获取行人轨迹，我们提出了一种新的跨摄像机时空模型，该模型集成了行人的行走习惯和摄像机之间的路径布局，以形成联合概率分布。这种跨摄像机时空模型可以使用稀疏采样的行人数据来指定。基于时空模型，可以通过条件随机场模型提取跨摄像机轨迹，并通过受限非负矩阵分解进一步优化。最后，提出了一种轨迹再排序技术来提高行人检索结果。为了验证我们方法的有效性，我们在真实监控场景中构建了第一个跨摄像机行人轨迹数据集，即 Person Trajectory Dataset。大量实验验证了所提出方法的有效性和鲁棒性。
