论文标题
深层重新识别模型的心理物理评估
Psychophysical Evaluation of Deep Re-Identification Models
论文作者
论文摘要
行人重新识别（REID）是跨时间和相机视图不断识别同一个人的任务。行人Reid和Hisgpus的研究人员花费了巨大的能量，以产生新颖的算法，挑战性数据集和易于使用的工具，以成功地改善标准指标的结果。生物识别技术，监视和自动驾驶的研究人员没有重新校定的益处，这些益处没有反映这些群体的益处。不同的检测，轻微的闭塞，视角变化和其他平庸的扰动使最佳的神经网络几乎没有用。这项工作有两个贡献。首先，我们将里德社区介绍了模型评估中计算机视觉研究的新兴领域。通过调整PSY神学的心理物理评估的既定原则，我们可以量化绩效降级并开始研究，以改善行人REID模型的实用性；不仅仅是他们的性能套装。其次，我们介绍了Nuscenesreid，这是一个具有挑战性的新REID数据，该数据旨在反映现实的自动驾驶汽车条件，其中使用了reidalgorithms。我们表明，尽管在现有的Reiddataset上表现良好，但大多数模型对于合成增强或对曲折的Nuscenesreid数据并不强大。
Pedestrian re-identification (ReID) is the task of continuously recognising the sameindividual across time and camera views. Researchers of pedestrian ReID and theirGPUs spend enormous energy producing novel algorithms, challenging datasets,and readily accessible tools to successfully improve results on standard metrics.Yet practitioners in biometrics, surveillance, and autonomous driving have not re-alized benefits that reflect these metrics. Different detections, slight occlusions,changes in perspective, and other banal perturbations render the best neural net-works virtually useless. This work makes two contributions. First, we introducethe ReID community to a budding area of computer vision research in model eval-uation. By adapting established principles of psychophysical evaluation from psy-chology, we can quantify the performance degradation and begin research thatwill improve the utility of pedestrian ReID models; not just their performance ontest sets. Second, we introduce NuscenesReID, a challenging new ReID datasetdesigned to reflect the real world autonomous vehicle conditions in which ReIDalgorithms are used. We show that, despite performing well on existing ReIDdatasets, most models are not robust to synthetic augmentations or to the morerealistic NuscenesReID data.
