Junejo Imran N, Ahmed Naveed, Lataifeh Mohammad
Zayed University, Dubai, United Arab Emirates.
University of Sharjah, Sharjah, United Arab Emirates.
Heliyon. 2021 Jun 30;7(6):e07422. doi: 10.1016/j.heliyon.2021.e07422. eCollection 2021 Jun.
Surveillance cameras are everywhere keeping an eye on pedestrians or people as they navigate through the scene. Within this context, our paper addresses the problem of pedestrian attribute recognition (PAR). This problem entails the extraction of different attributes such as age-group, clothing style, accessories, footwear style etc. This is a multi-label problem with a host of challenges even for human observers. As such, the topic has rightly attracted attention recently. In this work, we integrate trainable Gabor wavelet (TGW) layers inside a convolution neural network (CNN). Whereas other researchers have used fixed Gabor filters with the CNN, the proposed layers are learnable and adapt to the dataset for a better recognition. We test our method on publicly available challenging datasets and demonstrate considerable improvements over state of the art approaches.
监控摄像头无处不在，注视着行人或人们在场景中穿梭。在此背景下，我们的论文探讨行人属性识别（PAR）问题。该问题需要提取不同属性，如年龄组、服装风格、配饰、鞋类风格等。即使对于人类观察者而言，这也是一个具有诸多挑战的多标签问题。因此，该主题最近理所当然地受到了关注。在这项工作中，我们将可训练的伽柏小波（TGW）层集成到卷积神经网络（CNN）中。与其他研究人员在CNN中使用固定伽柏滤波器不同，我们提出的层是可学习的，并能适应数据集以实现更好的识别。我们在公开可用的具有挑战性的数据集上测试我们的方法，并证明与现有技术方法相比有显著改进。
