Affiliations: [a] Beijing Key Laboratory of Traffic Engineering, Beijing University of Technology, Beijing, China
| [b] School of Traffic and Transportation, Beijing Jiaotong University, Beijing, China
| [c] Institute of Computing Technologies, China Academy of Railway Sciences Corporation Limited, Beijing, China
Correspondence:
[*]
Corresponding author. Ye Zhang, Beijing Key Laboratory of Traffic Engineering, Beijing University of Technology, 100124, Beijing, China. E-mail: [email protected].
Abstract: The prevailing vehicle recognition technology is adversely affected by the environment such as complex traffic scenarios and weather conditions. This paper proposes a robust vehicle recognition model based on human memory mechanism named Memory-based Vehicle Recognition Model (MVRM). Motivated by the success of memory and attention mechanism, we explore some features of human visual attention model. Fusing short term and long-term memory modules together yield deeper architectures recognizing increasing complex environmental scenarios. Firstly, a rare motion feature has been introduced to measure the visual salience, which improves the accuracy of the visual attention mechanism. Second, a model of vehicle salient region recognition has been established. The results of experiments show that the dynamic vehicle recognition rate of MVRM is 77.10%, while its false recognition rate has only a nominal value of ∼4.5%. Furthermore, the model offers good recognition of vehicle targets under complex environment conditions related to weather and road traffic.
Keywords: Vehicle recognition, human memory, complex environment, cognitive
