基于Transformer的密集像素对应估计与基于检测的掩码实现多摄像机多行人关联
Multi-Camera Multi-Person Association using Transformer-Based Dense Pixel Correspondence Estimation and Detection-Based Masking
AI总结：
本研究提出一种结合Transformer密集像素对应估计与检测掩码的多摄像机多目标关联算法，经WILDTRACK数据集验证，近距同视角摄像机对关联效果优异，大差异视角仍有提升空间。
摘要：
多摄像机关联（Multi-camera Association, MCA）是跨摄像机视图识别物体与个体的任务，凭借在机器人、安防及农业领域的诸多应用，是当前活跃的研究方向。我们研究了一种新型多摄像机多目标关联算法，该算法基于Transformer架构的密集像素对应估计，并辅以基于检测的掩码机制。算法生成一组对应关键点后，计算摄像机视图中每对检测结果之间各关键点的置信度，进而确定包含每对检测匹配概率的亲和矩阵。最后，应用匈牙利算法生成最优分配矩阵，给出所有跨摄像机视图的预测关联结果。我们在WILDTRACK七摄像机高清数据集上评估了该方法，这是一个包含行走行人视频、精确标注及摄像机标定信息的高分辨率数据集。结果表明，该算法在位置相近、观察场景视角相似的摄像机对的行人关联任务中表现优异；而在距离或角度方向差异极大的摄像机对上，仍有较大提升空间。
英文摘要：
Multi-camera Association (MCA) is the task of identifying objects and individuals across camera views and is an active research topic, given its numerous applications across robotics, surveillance, and agriculture. We investigate a novel multi-camera multi-target association algorithm based on dense pixel correspondence estimation with a Transformer-based architecture and underlying detection-based masking. After the algorithm generates a set of corresponding keypoints and their respective confidence levels between every pair of detections in the camera views are computed, an affinity matrix is determined containing the probabilities of matches between each pair. Finally, the Hungarian algorithm is applied to generate an optimal assignment matrix with all the predicted associations between the camera views. Our method is evaluated on the WILDTRACK Seven-Camera HD Dataset, a high-resolution dataset containing footage of walking pedestrians as well as precise annotations and camera calibrations. Our results conclude that the algorithm performs exceptionally well associating pedestrians on camera pairs that are positioned close to each other and observe the scene from similar perspectives. On camera pairs with orientations that are drastically different in distance or angle, there is still significant room for improvement.
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