A Vehicle Recognition Algorithm Based on Deep Transfer Learning with a Multiple Feature Subspace Distribution

Sensors

Long Chen¹  Xiaobo Chen¹  Yingfeng Cai¹  Hai Wang²  Yijie Yu² 

[1] Automotive Engineering Research Institute, Jiangsu University, Zhenjiang 212013, China;School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang 212013, China;

关键词: vehicle recognition;    deep transfer learning;    multiple subspace feature distribution;    intelligent vehicles;   

DOI  :  10.3390/s18124109

来源: DOAJ

【 摘 要 】

Vehicle detection is a key component of environmental sensing systems for Intelligent Vehicles (IVs). The traditional shallow model and offline learning-based vehicle detection method are not able to satisfy the real-world challenges of environmental complexity and scene dynamics. Focusing on these problems, this work proposes a vehicle detection algorithm based on a multiple feature subspace distribution deep model with online transfer learning. Based on the multiple feature subspace distribution hypothesis, a deep model is established in which multiple Restricted Boltzmann Machines (RBMs) construct the lower layers and a Deep Belief Network (DBN) composes the superstructure. For this deep model, an unsupervised feature extraction method is applied, which is based on sparse constraints. Then, a transfer learning method with online sample generation is proposed based on the deep model. Finally, the entire classifier is retrained online with supervised learning. The experiment is actuated using the KITTI road image datasets. The performance of the proposed method is compared with many state-of-the-art methods and it is demonstrated that the proposed deep transfer learning-based algorithm outperformed existing state-of-the-art methods.

【 授权许可】

Unknown