Hu Haowen, Hachiuma Ryo, Saito Hideo, Takatsume Yoshifumi, Kajita Hiroki
Graduate School of Science and Technology, Keio University, Tokyo 223-8522, Japan.
Department of Anatomy, Keio University School of Medicine, Tokyo 160-8582, Japan.
J Imaging. 2022 Aug 17;8(8):219. doi: 10.3390/jimaging8080219.
Multi-camera multi-person (MCMP) tracking and re-identification (ReID) are essential tasks in safety, pedestrian analysis, and so on; however, most research focuses on outdoor scenarios because they are much more complicated to deal with occlusions and misidentification in a crowded room with obstacles. Moreover, it is challenging to complete the two tasks in one framework. We present a trajectory-based method, integrating tracking and ReID tasks. First, the poses of all surgical members captured by each camera are detected frame-by-frame; then, the detected poses are exploited to track the trajectories of all members for each camera; finally, these trajectories of different cameras are clustered to re-identify the members in the operating room across all cameras. Compared to other MCMP tracking and ReID methods, the proposed one mainly exploits trajectories, taking texture features that are less distinguishable in the operating room scenario as auxiliary cues. We also integrate temporal information during ReID, which is more reliable than the state-of-the-art framework where ReID is conducted frame-by-frame. In addition, our framework requires no training before deployment in new scenarios. We also created an annotated MCMP dataset with actual operating room videos. Our experiments prove the effectiveness of the proposed trajectory-based ReID algorithm. The proposed framework achieves 85.44% accuracy in the ReID task, outperforming the state-of-the-art framework in our operating room dataset.
多摄像头多人（MCMP）跟踪与重新识别（ReID）是安全、行人分析等领域的重要任务；然而，大多数研究集中在户外场景，因为在有障碍物的拥挤房间中处理遮挡和误识别要复杂得多。此外，在一个框架中完成这两项任务具有挑战性。我们提出了一种基于轨迹的方法，将跟踪和ReID任务集成在一起。首先，逐帧检测每个摄像头捕获的所有手术人员的姿态；然后，利用检测到的姿态来跟踪每个摄像头中所有人员的轨迹；最后，对不同摄像头的这些轨迹进行聚类，以重新识别手术室中所有摄像头的人员。与其他MCMP跟踪和ReID方法相比，所提出的方法主要利用轨迹，将在手术室场景中较难区分的纹理特征作为辅助线索。我们还在ReID过程中整合了时间信息，这比逐帧进行ReID的最先进框架更可靠。此外，我们的框架在部署到新场景之前无需训练。我们还使用实际手术室视频创建了一个带注释的MCMP数据集。我们的实验证明了所提出的基于轨迹的ReID算法的有效性。所提出的框架在ReID任务中达到了85.44%的准确率，在我们的手术室数据集中优于最先进的框架。
