Multi-Target Multi-Camera Tracking (MTMCT) tracks many people through video taken from several cameras. Person Re-Identification (Re-ID) retrieves from a gallery images of people similar to a person query image. We learn good features for both MTMCT and Re-ID with a convolutional neural network. Our contributions include an adaptive weighted triplet loss for training and a new technique for hard-identity mining. Our method outperforms the state of the art both on the DukeMTMC benchmarks for tracking, and on the Market-1501 and DukeMTMC-ReID benchmarks for Re-ID. We examine the correlation between good Re-ID and good MTMCT scores, and perform ablation studies to elucidate the contributions of the main components of our system. Code is available.
翻译:多目标多镜头跟踪(MTMCT)通过从若干摄像头摄取的视频跟踪许多人。个人身份识别(Re-ID)从像个人查询图像的人的画廊图像中检索到类似个人查询图像的人的图像。我们学习MTMCT和Re-ID与一个革命性神经网络的良好特征。我们的贡献包括培训的适应性加权三重损失和硬身份采矿的新技术。我们的方法优于DukeMMC追踪基准以及市场-1501和DukeMMMC-ReID再识别基准的先进水平。我们研究了良好的重新身份识别和良好的MTMCT评分之间的关系,并进行了反向研究,以阐明我们系统主要组成部分的贡献。我们有代码。