Vehicle detection and tracking based on video image processing in intelligent transportation system

Ge, Dongyuan; Yao, Xifan; Xiang, Wei; Chen, Yueping

doi:10.1007/s00521-022-06979-y

Cited by 22 publications

(14 citation statements)

References 24 publications

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“…The optical transmitting antenna, also called the transmitting telescope, shapes, and compresses the laser beam to become the required waveform and parameters, and shoots into the space to maximize the illumination energy obtained by the distant target. Traditional imaging lidars also require optomechanical scanning 24 . A pulse usually refers to a short-lived pulse-like electrical impulse (voltage or current) that is often used in electronic technology.…”

Section: Methodsmentioning

confidence: 99%

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(Retracted) Laser radar-based intelligent vehicle target recognition and detection system using image detection technology

Zheng

2022

J. Electron. Imag.

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.In recent years, with the progress of society and the development of the economy, the number of cars in China has been increasing. In wireless communication networks, the choice of wireless nodes has a greater impact on the improvement of system performance (such as channel capacity, coverage area, etc.). The intelligent vehicle target detection system can perceive and recognize the surrounding objects such as pedestrians and vehicles through sensors, which is the basis for realizing the unmanned driving of intelligent vehicles. In a wireless environment where multiple wireless nodes coexist, current research focuses on how long it takes to re-plan the selection of wireless nodes (for mobile environments) and how to allocate and manage wireless nodes, such as where under the conditions of wireless, who will wireless with whom, and in what way (such as auto focus, digital fusion, or other) wireless, etc.; the dedicated centralized controller (such as the base station of the infrastructure-based wireless access network) determines the wireless partner (i.e., centralized type selection), or the wireless node decides by itself (i.e., distributed selection); select the appropriate number of wireless nodes to take into account the system performance gain and implementation complexity. The popularity of automobiles has brought great convenience to people’s lives, but it has also brought greater traffic pressure. At present, the amount of domestic automobile traffic has increased exponentially, and urban traffic congestion has become more serious, and even caused serious traffic accidents, directly affecting people’s quality of life. The emergence of intelligent transportation systems has effectively alleviated road traffic pressure and reduced the incidence of traffic accidents. As an important part of the intelligent transportation system, the research of intelligent vehicles has received extensive attention. Intelligent vehicles use a variety of sensors installed on the body to sense the environment, and realize intelligent driving functions such as lane line detection, obstacle detection, dynamic cruise control, and unmanned driving, which is conducive to reducing the incidence of traffic accidents and improving the safety of vehicle driving. At present, in the research of target recognition and tracking of intelligent vehicles, the traditional target detection method is mainly based on artificial feature extraction, which is difficult to describe more complex or higher-order image features, and the tracking effect is not good, which limits the target detection. An intelligent vehicle is an intelligent system with functions such as environment perception, planning decision-making, and operation control, and is an important part of the intelligent transportation system. Identify the effect. In response to these problems, we present a study of the target recognition and tracking of intelligent vehicles based on grid map and lidar sensor technology. To verify the proposed method in the actual scene, we identify ...

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Section: Methodsmentioning

confidence: 99%

“…Traditional imaging lidars also require optomechanical scanning. 24 A pulse usually refers to a short-lived pulse-like electrical impulse (voltage or current) that is often used in electronic technology. The main characteristics are waveform, amplitude, width, and repetition rate.…”

Section: Launch Partmentioning

confidence: 99%

(Retracted) Laser radar-based intelligent vehicle target recognition and detection system using image detection technology

Zheng

2022

J. Electron. Imag.

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“…After formulating state and observation sets, we can derive the prediction and update equations of the Bayes filter over intensity functions. These equations are shown in equation (10,11) [14]: The prediction equation in equation (10) calculates the predicted intensity function of the target states at time t+1, given the intensity function at time t [14]. It is composed of three terms: the first term is the predicted intensity of the target states that remain in the surveillance region from the previous time step; the second term is the predicted intensity of the target states that are newly born in the surveillance region; and the third term is the predicted intensity of the target states that have survived until time t+1 but have left the surveillance region and then re-entered.…”

Section: Phd Filtermentioning

confidence: 99%

Robust Multi-Target Tracking with Integrated Knowledge Model, Graph Structures, and Statistical Analysis in Unobservable Environments

Mehrizi,

Yazdi

2024

Preprint

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This paper presents an innovative approach to vehicle tracking in video streams, utilizing graph structures, integrated prior knowledge, and forecasting techniques. The method encompasses statistical analysis and prediction of non-identical periodic events, specifically tailored to the characteristics of vehicles in motion. Through the generation of pseudo-observations, auxiliary information is provided to the tracker, effectively capturing the underlying dynamics of vehicle movements. The approach is evaluated on tracking datasets, demonstrating substantial improvements in tracking performance, especially in scenarios with camera sensor occlusion caused by physical obstructions, which is a common challenge encountered in camera-based tracking systems.

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“…ITS processes the information acquired by surveillance cameras to automate traffic monitoring, thereby guiding the traffic authorities to take advert possible mishaps caused by commuters. The acquired surveillance footage is useful in ITS to carry out vehicle detection [4] [5], vehicle counting [6], vehicle re-identification [1] and tracking [7] [8]. Currently, more efforts are put in place in computer vision to develop a robust vehicle reidentification framework.…”

Section: Introductionmentioning

confidence: 99%

Enhanced Vehicle Re-Identification for Smart City Applications Using Zone Specific Surveillance

et al. 2023

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Vehicle re-identification is an important feature of an intelligent transportation system as part of a smart city application. Vehicle re-identification aims at matching vehicles from images acquired by surveillance cameras at different locations. During rush hours, vehicles are densely occupied across regions such as entry/exit of gated campuses, railways, airports, educational institutions, etc. Due to this uneven flow of traffic, there is a possibility of violation of traffic rules by the vehicles that lead to a security breach. In such scenarios to speed up the re-identification process, it is justified to look into a specific group of surveillance cameras to detect and re-identify vehicles on day to day basis in near real-time. However, the existing vehicle re-identification datasets do not contain zone specific information and therefore can not be used to evaluate the performance of re-identification algorithms in different zones. In the proposed work for re-identification, a framework is developed that performs vehicle re-identification across a group of cameras that monitors varying traffic movements over an area. These areas defined as "strategic zones" comprise a subset of non-overlapping cameras that are installed to monitor non-uniform vehicle movements. The reidentification framework is evaluated on a novel dataset developed to understand the performance of vehicle re-identification across strategic zones. The dataset consists of videos of vehicles captured through 20 CCTV surveillance cameras that are grouped into four different zones. Various experiments are conducted to study the performance of re-identification across four zones using a deep neural network with triplet loss, L2 regularization, and re-ranking. The experiments conducted with an image dimension of 224 × 224 have demonstrated an overall mAP of 77.22%. Also, for each of the four zones a mAP of 82.16%, 69.1%, 66.5%, and 75.76% is achieved. The experimental results demonstrate huge variations in the accuracy of vehicle re-identification method across different zones. Therefore, the study assess the possible measures that can be taken to improve the performance in individual zones for an accurate vehicle re-identification in intelligent transport system.

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Vehicle detection and tracking based on video image processing in intelligent transportation system

Cited by 22 publications

References 24 publications

(Retracted) Laser radar-based intelligent vehicle target recognition and detection system using image detection technology

(Retracted) Laser radar-based intelligent vehicle target recognition and detection system using image detection technology

Robust Multi-Target Tracking with Integrated Knowledge Model, Graph Structures, and Statistical Analysis in Unobservable Environments

Enhanced Vehicle Re-Identification for Smart City Applications Using Zone Specific Surveillance

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