Last Decade in Vehicle Detection and Classification: A Comprehensive Survey

Maity, Sourajit; Bhattacharyya, Avirup; Singh, Pawan Kumar; Kumar, Munish; Sarkar, Ram

doi:10.1007/s11831-022-09764-1

Cited by 15 publications

(5 citation statements)

References 119 publications

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“…Currently, the majority of research that has been published mainly focuses on grouping vehicles into broad groups, including motorbikes, automobiles, minibuses, or tankers [ 12 ]. However, more versatility is needed to meet user requests.…”

Section: Related Workmentioning

confidence: 99%

Vehicle Detection and Recognition Approach in Multi-Scale Traffic Monitoring System via Graph-Based Data Optimization

Wieczorek

Tahir

Akhter

et al. 2023

Sensors

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Over the past few years, significant investments in smart traffic monitoring systems have been made. The most important step in machine learning is detecting and recognizing objects relative to vehicles. Due to variations in vision and different lighting conditions, the recognition and tracking of vehicles under varying extreme conditions has become one of the most challenging tasks. To deal with this, our proposed system presents an adaptive method for robustly recognizing several existing automobiles in dense traffic settings. Additionally, this research presents a broad framework for effective on-road vehicle recognition and detection. Furthermore, the proposed system focuses on challenges typically noticed in analyzing traffic scenes captured by in-vehicle cameras, such as consistent extraction of features. First, we performed frame conversion, background subtraction, and object shape optimization as preprocessing steps. Next, two important features (energy and deep optical flow) were extracted. The incorporation of energy and dense optical flow features in distance-adaptive window areas and subsequent processing over the fused features resulted in a greater capacity for discrimination. Next, a graph-mining-based approach was applied to select optimal features. Finally, the artificial neural network was adopted for detection and classification. The experimental results show significant performance in two benchmark datasets, including the LISA and KITTI 7 databases. The LISA dataset achieved a mean recognition rate of 93.75% on the LDB1 and LDB2 databases, whereas KITTI attained 82.85% accuracy on separate training of ANN.

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Section: Related Workmentioning

confidence: 99%

Vehicle Detection and Recognition Approach in Multi-Scale Traffic Monitoring System via Graph-Based Data Optimization

Wieczorek

Tahir

Akhter

et al. 2023

Sensors

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“…The most commonly used supervised algorithms are Artificial Neural Networks (ANNs), Support Vector Machines (SVMs), Discriminant Analysis (DA), Naive Bayes (NB), Random Forests (RFs), decision trees (DTs), and K-Nearest Neighbors (KNNs) [11][12][13]. The selection of a suitable ML algorithm is of prime importance in solving the difficult task of finding a solution to a given classification problem [14,15]. The selection of network/model parameters, such as the input, target, size of the training dataset, and regularization parameters, determines the performance of the ML algorithm.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Real-Time Vehicle Classification from a Complex Image Dataset Using eXtreme Gradient Boosting and the Multi-Objective Genetic Algorithm

Mani,

Rakkiya Goundar Komarasamy,

Rajamanickam

et al. 2024

Processes

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Recent advancements in image processing and machine-learning technologies have significantly improved vehicle monitoring and identification in road transportation systems. Vehicle classification (VC) is essential for effective monitoring and identification within large datasets. Detecting and classifying vehicles from surveillance videos into various categories is a complex challenge in current information acquisition and self-processing technology. In this paper, we implement a dual-phase procedure for vehicle selection by merging eXtreme Gradient Boosting (XGBoost) and the Multi-Objective Optimization Genetic Algorithm (Mob-GA) for VC in vehicle image datasets. In the initial phase, vehicle images are aligned using XGBoost to effectively eliminate insignificant images. In the final phase, the hybrid form of XGBoost and Mob-GA provides optimal vehicle classification with a pioneering attribute-selection technique applied by a prominent classifier on 10 publicly accessible vehicle datasets. Extensive experiments on publicly available large vehicle datasets have been conducted to demonstrate and compare the proposed approach. The experimental analysis was carried out using a myRIO FPGA board and HUSKY Lens for real-time measurements, achieving a faster execution time of 0.16 ns. The investigation results show that this hybrid algorithm offers improved evaluation measures compared to using XGBoost and Mob-GA individually for vehicle classification.

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“…There are many methods of detecting vehicles on roads [8]. The most efficient methods use complex devices, such as cameras [9] or even drones [10].…”

Section: Introductionmentioning

confidence: 99%

Low-cost modular devices for on-road vehicle detection and characterisation

Poza-Lujan

Uribe-Chavert

Posadas-Yagüe

2023

Des Autom Embed Syst

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Detecting and characterising vehicles is one of the purposes of embedded systems used in intelligent environments. An analysis of a vehicle’s characteristics can reveal inappropriate or dangerous behaviour. This detection makes it possible to sanction or notify emergency services to take early and practical actions. Vehicle detection and characterisation systems employ complex sensors such as video cameras, especially in urban environments. These sensors provide high precision and performance, although the price and computational requirements are proportional to their accuracy. These sensors offer high accuracy, but the price and computational requirements are directly proportional to their performance. This article introduces a system based on modular devices that is economical and has a low computational cost. These devices use ultrasonic sensors to detect the speed and length of vehicles. The measurement accuracy is improved through the collaboration of the device modules. The experiments were performed using multiple modules oriented to different angles. This module is coupled with another specifically designed to detect distance using previous modules’ speed and length data. The collaboration between different modules reduces the speed relative error ranges from 1 to 5%, depending on the angle configuration used in the modules.

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Last Decade in Vehicle Detection and Classification: A Comprehensive Survey

Cited by 15 publications

References 119 publications

Vehicle Detection and Recognition Approach in Multi-Scale Traffic Monitoring System via Graph-Based Data Optimization

Vehicle Detection and Recognition Approach in Multi-Scale Traffic Monitoring System via Graph-Based Data Optimization

An Efficient Real-Time Vehicle Classification from a Complex Image Dataset Using eXtreme Gradient Boosting and the Multi-Objective Genetic Algorithm

Low-cost modular devices for on-road vehicle detection and characterisation

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