具有遮挡鲁棒性的监控视频人脸再识别算法

Surveillance Video Re-Identification with Robustness to Occlusion

传统身份识别技术需要将待识别人员信息预先录入,同时未考虑识别过程中的遮挡问题,不能满足公共场所基于监控视频的再识别需求。现有行人再识别算法多依赖于服饰等外观特征,难以进行长期追踪与再识别。针对以上问题,本文提出了一种对遮挡具有鲁棒性的人脸再识别算法。首先,对监控视频中的人脸进行检测与对齐,并判断人脸中存在的遮挡位置;其次,根据遮挡位置查找掩码字典并选择对应掩码,再用掩码排除遮挡元素;最后,使用注意力机制对多帧图片分配权重以更新特征,再使用分区域匹配方法得到识别结果。为验证该方法的有效性,本文分别在COX数据集和人工合成遮挡的数据集上对所提方法进行了测试。其中,在COX数据集上的rank-1准确率为95.2%,在合成遮挡的数据集上rank-1准确率为73.0%,相比现有方法有明显优势。

Traditional identification technologies require pre-recorded information from target personals,while failing to consider any visual obstructions in the identification process,resulting in its unsatisfactory performance in surveillance-video-based reidentification scenarios,especially for public spaces. Most existing person re-identification approaches examine appearance features such as clothing and decoration,which are prone to change in time and space,and thus are unreliable for long-term tracking. An effective and reliable approach for long-term re-identification is to utilize stable biometric features such as facial features. However,with occlusion,low resolution,lack of illumination,and perspective gestures exhibited in surveillance videos,traditional facial recognition methods that are excellent for image recognition cannot perform well. To address these issues,this paper proposed a deep-learning-based face re-identification algorithm. The algorithm combined an attention mechanism with a mask dictionary to dynamically and appropriately assign weights to video frame features,thereby reducing the effect of occlusion and effectively improving the re-identification accuracy. Extensive experiments demonstrated that the proposed method was able to achieve a rank-1 accuracy of up to 95. 2% on the cox dataset,and 73. 0% on the same dataset with synthetic occlusion. These results comfirm the superior performance of the proposed algorithm compared to state-of-the-art re-identification algorithms.