Development of AI-Based Vehicle Detection and Tracking System for C-ITS Application

Tak, Sehyun; Lee, Jong-Deok; Song, Jiankang; Kim, Sunghoon

doi:10.1155/2021/4438861

Cited by 17 publications

(7 citation statements)

References 42 publications

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“…In a wide range of AI (artificial intelligence)-enabled service fields such as human–computer interaction [ 1 ], traffic control [ 2 ], video surveillance [ 3 ], and augmented reality [ 4 ], object-tracking technology has drawn constant attention. Object tracking is largely divided into detection-free tracking and tracking-by-detection.…”

Section: Introductionmentioning

confidence: 99%

Scheduling Framework for Accelerating Multiple Detection-Free Object Trackers

Kim

Yong

et al. 2023

Sensors

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In detection-free tracking, after users freely designate the location of the object to be tracked in the first frame of the video sequence, the location of the object is continuously found in the following video frame sequence. Recently, technologies using a Siamese network and transformer based on DNN modules have been evaluated as very excellent in terms of tracking accuracy. The high computational complexity due to the usage of the DNN module is not a preferred feature in terms of execution speed, and when tracking two or more objects, a bottleneck effect occurs in the DNN accelerator such as the GPU, which inevitably results in a larger delay. To address this problem, we propose a tracker scheduling framework. First, the computation structures of representative trackers are analyzed, and the scheduling unit suitable for the execution characteristics of each tracker is derived. Based on this analysis, the decomposed workloads of trackers are multi-threaded under the control of the scheduling framework. CPU-side multi-threading leads the GPU to a work-conserving state while enabling parallel processing as much as possible even within a single GPU depending on the resource availability of the internal hardware. The proposed framework is a general-purpose system-level software solution that can be applied not only to GPUs but also to other hardware accelerators. As a result of confirmation through various experiments, when tracking two objects, the execution speed was improved by up to 55% while maintaining almost the same accuracy as the existing method.

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Section: Introductionmentioning

confidence: 99%

Scheduling Framework for Accelerating Multiple Detection-Free Object Trackers

Kim

Yong

et al. 2023

Sensors

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“…Part 2 contains the related work, part 3 includes the proposed method explained, part 4 presents the results and part 5 discusses the results. Tak (2021) offered research to obtain traffic information from a surveillance device placed at a junction, enhancing road management systems. As a result, the technique suggested in this research demonstrates the viability of gathering thorough roadway information utilizing cameras mounted at a junction.…”

Section: Introductionmentioning

confidence: 99%

AI-based vehicular traffic flow forecast for advanced transportation systems

Panwar,

Khan,

Ganesh

2023

Multidiscip. Sci. J.

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For traffic control systems, the intricate nature and quantity of vehicular traffic have recently risen significantly. For transportation systems to be optimized and overall efficiency to be increased, accurate prediction of traffic is essential. To anticipate vehicular traffic flow, we introduce a unique reptile-ant optimized bidirectional gated recurrent unit (RAO-BiGRU) method in this study. Ant colony optimization (ACO) and reptile search optimization (RSO) methods are combined in the RAO. The Bi-GRU model's performance is improved by using the RAO to better choose the input features. Extensive experiments are run utilizing the traffic data database to assess the efficacy of the suggested approach. The outcomes show that in terms of predicting accuracy and computational effectiveness, the suggested RAO-BiGRU strategy performs better than the conventional forecasting strategies.

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“…Travel speed is a critical metric in road traffic analysis [1], commonly used to quantify the average velocity of vehicles over specific roadway segments. This metric serves multiple functions: it can gauge a driver's behavior, such as speeding or slow driving [2], [3], thereby evaluating the safety of their actions [4], [5].…”

Section: Introductionmentioning

confidence: 99%

A Framework for Travel Speed Prediction Inclusive of Service Area Dwell Times

Luo,

Zou,

Luo

et al. 2023

IEEE Access

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Accurate modeling of travel speeds is crucial for optimizing roadway management, yet traditional methods overlook a key factor the influence of vehicle dwell times in service areas. This oversight introduces bias into speed measurements, impairing their utility for fine-grained traffic monitoring. To address this problem, we propose an innovative framework that integrates machine learning prediction of service area dwell times into travel speed calculation. We focus on a 9.3 km segment of a major highway in Fujian Province, China that includes the Qingyunshan service area. A Gradient Boosting Decision Tree model identifies vehicles entering the service area, while a Bayesian Backpropagation Neural Network predicts their dwell time. By adjusting the overall travel times using these predicted dwell times, our approach recovers normal driving behavior outside service areas. Experiments on electronic toll collection data from over 17 million transactions validate the framework's effectiveness. The corrected travel speeds better reflect typical highway conditions and enable more precise assessment of traffic state across multiple time horizons. This study highlights the vital role of service area dwell time in travel speed modeling. Our solution provides a promising direction to enhance the fidelity of current prediction practices. INDEX TERMStravel speed, machine learning, service area, dwell time, traffic state This article has been accepted for publication in IEEE Access.

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Development of AI-Based Vehicle Detection and Tracking System for C-ITS Application

Cited by 17 publications

References 42 publications

Scheduling Framework for Accelerating Multiple Detection-Free Object Trackers

Scheduling Framework for Accelerating Multiple Detection-Free Object Trackers

AI-based vehicular traffic flow forecast for advanced transportation systems

A Framework for Travel Speed Prediction Inclusive of Service Area Dwell Times

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