Towards AI-Based Traffic Counting System with Edge Computing

Abstract

The recent years have witnessed a considerable rise in the number of vehicles, which has placed transportation infrastructure and traffic control under tremendous pressure. Yielding timely and accurate traffic flow information is essential in the development of traffic control strategies. Despite the continual advances and the wealth of literature available in intelligent transportation system (ITS), there is a lack of practical traffic counting system, which is readily deployable on edge devices. In this study, we introduce a low-cost and effective edge-based system integrating object detection models to perform vehicle detecting, tracking, and counting. First, a vehicle detection dataset (VDD) representing traffic conditions in Vietnam was created. Several deep learning models for VDD were then examined on two different edge device types. Using this detection, we presented a lightweight counting method seamlessly combining with a traditional tracking method to increase counting accuracy. Finally, the traffic flow information is obtained based on counted vehicle categories and their directions. The experiment results clearly indicate that the proposed system achieves the top inference speed at around 26.8 frames per second (FPS) with 92.1% accuracy on the VDD. This proves that our proposal is capable of producing high-accuracy traffic flow information and can be applicable to ITS in order to reduce labor-intensive tasks in traffic management.

Application of Artificial Intelligence Technology in Computer Network Security Communication

“…In the data preprocessing step, the input traffic is divided into a flow and the packets in the flow are reassembled into messages. Protocol keyword extraction is mainly carried out in two steps [5]. Liu and Yangjun proposed that in the first frequency string extraction step, the Apriori algorithm be used to input and extract message sequences from field format candidate keywords.…”

Deep Neural Network-based Approach for Accurate Vehicle Counting

“…However, the aforementioned techniques are insufficient and easily influenced by the big rotation in the image. As a result, adjusting the camera angle has a considerable influence on their detection accuracy [12]. Several object detectors based on CNN have recently been proposed, resulting in improved object detection performance.…”

Object Detection in Traffic Videos: A Survey

“…The object detection methods should be configured in a way that complies with the real-time requirements to be applicable in real-world systems. There have been numerous studies attempting to increase the efficiency of object detection if traffic surveillance applications on edge computing platforms [46], [246]- [250]. However, enabling the object detection algorithms, especially methods that are designed based on deep learning, to achieve the desired real-time characteristics is still an open challenge that requires substantial effort.…”