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:我们致力于从非重叠式摄像头网络拍摄的多个视频中检索查询人物。现有方法往往依靠纯视觉匹配或考虑时间约束,但忽略了摄像头网络的空间信息。为了解决这个问题,我们提出了一种基于跨相机轨迹生成的行人检索框架,该框架集成了时空信息。为了获得行人轨迹,我们提出了一种新的跨相机时空模型,该模型将行人的行走习惯和摄像机之间的路径布局整合成一个联合概率分布。在相机网络中,这种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相机轨迹,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,以改善行人检索结果。为了验证我们方法的有效性,我们构建了第一个跨相机行人轨迹数据集: 人物轨迹数据集,在实际监视场景中进行了评估。广泛的实验验证了所提出方法的有效性和鲁棒性。