当前位置: X-MOL 学术J. Real-Time Image Proc. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Multi-level feature fusion model-based real-time person re-identification for forensics
Journal of Real-Time Image Processing ( IF 3 ) Pub Date : 2019-09-17 , DOI: 10.1007/s11554-019-00908-4
Shiqin Wang , Xin Xu , Lei Liu , Jing Tian

Person forensics aims to retrieve the specified person across non-overlapping cameras. It is difficult owing to the appearance variations caused by occlusion, human pose change, background clutter, illumination variation, etc. In this scenario, current models face great challenges in extracting effective features. Recent deep learning models mainly focus on extracting representative deep features to cope with appearance variations, while handcrafted features are not fully explored. In this paper, a multi-level feature fusion model (MFFM) is designed to combine both deep features and handcrafted features in real time. MFFM is first utilized to describe person appearance. Then, local binary pattern (LBP) and histogram of oriented gradient (HOG) are extracted to cope with geometric change and illumination variance. Using LBP and HOG, 11.89% on the CUHK03, 15.30% on the Market-1501 and 8.25% on the VIPeR top-1 recognition accuracy improvement for the proposed method are achieved with only 9.66%, 4.90%, and 7.59% extra processing time. Experimental results indicate MFFM can achieve the best performance compared to the state-of-the-art models on the Market1501, CUHK03, and VIPeR datasets.

中文翻译:

基于多层特征融合模型的取证人员实时识别

人员取证的目的是在不重叠的摄像机之间检索指定的人员。由于遮挡,人体姿势变化,背景杂波,照明变化等导致的外观变化很困难。在这种情况下,当前模型在提取有效特征方面面临巨大挑战。近期的深度学习模型主要集中在提取代表性的深度特征以应对外观变化,而手工特征并未得到充分探索。在本文中,设计了一个多级特征融合模型(MFFM),以实时结合深度特征和手工特征。MFFM首先用于描述人的外表。然后,提取局部二进制图案(LBP)和定向梯度直方图(HOG)以应对几何变化和照明差异。使用LBP和HOG,11。CUHK03的89%,Market-1501的15.30%和VIPeR top-1识别精度的8.25%的改进仅用了9.66%,4.90%和7.59%的额外处理时间即可实现。实验结果表明,与Market1501,CUHK03和VIPeR数据集上的最新模型相比,MFFM可以实现最佳性能。
更新日期:2019-09-17
down
wechat
bug