在安检点使用多台顶视摄像头追踪乘客与行李物品
Tracking Passengers and Baggage Items Using Multiple Overhead Cameras at Security Checkpoints
AI总结：
针对机场安检顶视摄像头的乘客与行李追踪需求，提出融合自监督学习检测、单摄追踪与多视图关联的框架，显著提升检测与多目标追踪精度且推理速度快。
摘要：
我们提出了一种适用于机场安检场景下顶视摄像头视频多目标追踪的新型框架，场景中的追踪目标为乘客及其行李物品。我们提出一种自监督学习（self-supervised learning，SSL）技术，为模型提供来自顶视图像的实例分割不确定性信息。我们的SSL方法通过采用测试时数据增强以及基于回归的旋转不变伪标签细化技术来提升目标检测效果。我们的伪标签生成方法将多张经过几何变换的图像作为卷积神经网络（convolutional neural network，CNN）的输入，对网络生成的增强检测结果进行回归以降低定位误差，随后使用均值漂移（mean-shift）算法对其进行聚类。该自监督检测器模型被应用于单摄像头追踪算法中，用于为目标生成时间标识符。我们的方法还融合了多视图轨迹关联机制，以在乘客穿行于不同摄像头视野时维持一致的时间标识符。我们在真实机场安检环境下的多台顶视摄像头采集的视频上，对检测、追踪和关联性能进行了评估，证明了所提方法的有效性。结果表明，自监督学习将目标检测准确率最高提升了42%，且未增加模型的推理时间。我们的多摄像头关联方法实现了最高89%的多目标追踪准确率，平均计算耗时低于15毫秒。
英文摘要：
We introduce a novel framework to track multiple objects in overhead camera videos for airport checkpoint security scenarios where targets correspond to passengers and their baggage items. We propose a self-supervised learning (SSL) technique to provide the model information about instance segmentation uncertainty from overhead images. Our SSL approach improves object detection by employing a test-time data augmentation and a regression-based, rotation-invariant pseudo-label refinement technique. Our pseudo-label generation method provides multiple geometrically transformed images as inputs to a convolutional neural network (CNN), regresses the augmented detections generated by the network to reduce localization errors, and then clusters them using the mean-shift algorithm. The self-supervised detector model is used in a single-camera tracking algorithm to generate temporal identifiers for the targets. Our method also incorporates a multiview trajectory association mechanism to maintain consistent temporal identifiers as passengers travel across camera views. An evaluation of detection, tracking, and association performances on videos obtained from multiple overhead cameras in a realistic airport checkpoint environment demonstrates the effectiveness of the proposed approach. Our results show that self-supervision improves object detection accuracy by up to 42% without increasing the inference time of the model. Our multicamera association method achieves up to 89% multiobject tracking accuracy with an average computation time of less than 15 ms.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
