Abstract
The intelligent transportation system under the big data environment is the development direction of the future transportation system. It effectively integrates advanced information technology, data communication transmission technology, electronic sensing technology, control technology, and computer technology and applies them to the entire ground transportation management system to establish a real-time, accurate, and efficient comprehensive transportation management system that works on a large scale and in all directions. Intelligent video analysis is an important part of smart transportation. In order to improve the accuracy and time efficiency of video retrieval schemes and recognition schemes, this article firstly proposes a segmentation and key frame extraction method for video behavior recognition, using a multi-time scale dual-stream network to extract video features, improving the efficiency and efficiency of video behavior detection. On this basis, an improved algorithm for vehicle detection based on Faster R-CNN is proposed, and the Faster R-CNN network feature extraction layer is improved by using the principle of residual network, and a hole convolution is added to the network to filter out the redundant features of high-resolution video images to improve the problem of vehicle missed detection in the original algorithm. The experimental results show that the key frame extraction technology combined with the optimized Faster R-CNN algorithm model greatly improves the accuracy of detection and reduces the leakage. The detection rate is satisfactory.
Highlights
Intelligent transportation is based on smart transportation
From the 1970s to the 1980s, intelligent transportation was proposed as a concept, but it was limited by computing power and communication means, and its development speed was slow
In order to improve the accuracy of traffic smart video recognition, this paper proposes the use of key frame technology set combined with Faster R-CNN vehicle detection algorithm to judge vehicles
Summary
Since the end of the last century, with the great development of data transmission speed, computing power, and positioning technology, the development speed of intelligent transportation has been greatly increased. Some developed countries such as the United States, Japan, and Europe have turned their research perspectives to verify. A large amount of road monitoring video equipment is built, and massive video data is accumulated in the traffic video system, which puts forward higher requirements for the storage capacity, transmission bandwidth, data analysis, and abnormal situation identification of the system. Big data-related technologies are required to conduct in-depth mining and development of relevant data and adopt more advanced recognition methods to realize data sharing and integration, to achieve the purpose of intelligent services
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