Abstract
Throughout the past decade, vehicular networks have attracted a great deal of interest in various fields. The increasing number of vehicles has led to challenges in traffic regulation. Vehicle-type detection is an important research topic that has found various applications in numerous fields. Its main purpose is to extract the different features of vehicles from videos or pictures captured by traffic surveillance so as to identify the types of vehicles, and then provide reference information for traffic monitoring and control. In this paper, we propose a step-forward vehicle-detection and -classification method using a saliency map and the convolutional neural-network (CNN) technique. Specifically, compressed-sensing (CS) theory is applied to generate the saliency map to label the vehicles in an image, and the CNN scheme is then used to classify them. We applied the concept of the saliency map to search the image for target vehicles: this step is based on the use of the saliency map to minimize redundant areas. CS was used to measure the image of interest and obtain its saliency in the measurement domain. Because the data in the measurement domain are much smaller than those in the pixel domain, saliency maps can be generated at a low computation cost and faster speed. Then, based on the saliency map, we identified the target vehicles and classified them into different types using the CNN. The experimental results show that our method is able to speed up the window-calibrating stages of CNN-based image classification. Moreover, our proposed method has better overall performance in vehicle-type detection compared with other methods. It has very broad prospects for practical applications in vehicular networks.
Highlights
Throughout the past decade, vehicular networks have captured a great deal of interest in both the industrial and academic fields
We introduced the concept of a saliency map, on the basis of which the image is searched for target vehicles
The test results indicate that whether the size of the image block is two or 16, saliency detection in on our method is very close to the effect achieved using an ideal saliency map
Summary
Throughout the past decade, vehicular networks have captured a great deal of interest in both the industrial and academic fields. In the 1970s, only a magnetic coil could be used to detect vehicle types; radar, ultrasonic, and infrared images, and surveillance videos, are widely used for detection [1]. With an increasing number of digital video-surveillance devices widely deployed on traffic roads and vehicles, vehicle vision-detection methods have become an actively researched topic in recent years. Vehicle-type detection is an important research topic with diverse applications in intelligent. The emergence of CS has tremendously affected signal acquisition and signal recovery [4,5,6], because signal compressibility or sparsity is of great significance. A signal may have no sparsity in some domains. We can always find a certain domain where signal x can be considered sparse with an appropriate basis
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