Traffic State Recognition of Intersection Based on Image Model and PCA Hashing

Abstract

The premise of implementing an effective traffic control strategy is the accurate traffic state recognition. In the existing study, traffic state recognition methods were processed by using statistical characteristics and long-term scale detection of field traffic data. Hence, the dynamic characteristics and subtle changes in traffic flow were easy to overlook. At present, more and more advanced traffic detection technology provides reliable and accurate data for measuring and distinguishing the state of urban road traffic, such as the cooperative vehicle-infrastructure system, wide-area radar technology, and 5G technology. This study proposes a novel method called HTSI (High Precision Traffic State Identification Method), which is based on the advanced detection technology in traffic state recognition at the intersection: The raw data used for intersection traffic state recognition is high-precision detection data of tracking characteristics, which make the data look like a picture of the intersection at God’s perspective. To this end, we construct an image model for intersections and implement image feature extraction in a way that is different from traditional image processing. Then, the traffic state recognition problem at the intersection is translated into an image searching problem with tags. The image searching is realized by the hashing algorithm. Finally, the comprehensive experiments prove that the proposed method is more accurate and finer than other methods.

Una revisión sistemática de literatura sobre implementaciones de sistemas de control de tráfico

“…Es un tipo de algoritmo de aprendizaje profundo que se puede utilizar para aprender en base de datos representados como grafos. (Shin et al, 2019); (Chabchoub et al, 2021); (Wakkumbura et al, 2021); (Zhang et al, 2020); (Li, Y. et al, 2021) Image Processing Es un método que utiliza un conjunto de técnicas para manipular y analizar imágenes digitales. (Bao et al, 2023); (Cheng et al, 2023); (Hao, W. et al, 2020); (Islam et al, 2022); (Chahal et al, 2023) Long Short-Term Memory (LSTM)…”

“…Modelos de reconocimiento de objetos utilizados en identificación de densidad vehicular REFERENCIA MODELO DESCRIPCIÓN (Shin et al, 2019); (Chabchoub et al, 2021); (Wakkumbura et al, 2021); (Zhang et al, 2020); (Li, Y. et al, 2021) You Only Look Once (YOLO)…”