Transfer Learning Using Deep Neural Networks for Classification of Truck Body Types Based on Side-Fire Lidar Data

Nezafat, Reza; Sahin, Olcay; Cetin, Mecit

doi:10.1007/s42421-019-00005-9

Cited by 29 publications

(11 citation statements)

References 40 publications

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“…This is possible due to the multiple hidden layers which can capture sophisticated nonlinear representations in a dataset. In [91], a TL approach using deep neural networks was proposed for vehicle classification. The authors investigated the possibility of the TL of a pre-trained CNN model's parameters for classifying truck images generated from 3D point cloud data from LiDAR.…”

Section: Neural Network Transfer Learning Methodsmentioning

confidence: 99%

A Survey of Vision-Based Transfer Learning in Human Activity Recognition

2021

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Human activity recognition (HAR) and transfer learning (TL) are two broad areas widely studied in computational intelligence (CI) and artificial intelligence (AI) applications. Much effort has been put into developing suitable solutions to advance the current performance of existing systems. However, challenges are facing the existing methods of HAR. In HAR, the variations in data required in HAR systems pose challenges to many existing solutions. The type of sensory information used could play an important role in overcoming some of these challenges. Vision-based information in 3D acquired using RGB-D cameras is one type. Furthermore, with the successes encountered in TL, HAR stands to benefit from TL to address challenges to existing methods. Therefore, it is important to review the current state-of-the-art related to both areas. This paper presents a comprehensive survey of vision-based HAR using different methods with a focus on the incorporation of TL in HAR methods. It also discusses the limitations, challenges and possible future directions for more research.

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Section: Neural Network Transfer Learning Methodsmentioning

confidence: 99%

A Survey of Vision-Based Transfer Learning in Human Activity Recognition

2021

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“…Since the data recorded by driver behavior (via smartphone sensors) are time-series big data, the use of Deep Learning-based approaches can be very accurate. Deep and reinforcing learning methods are used in various areas of transportation systems such as predicting Macroscopic Traffic Congestion [38]- [42], Transportation System Planning [38], [39], [43], customer demand forecast for transportation [43]- [46], traffic monitoring and congestion detection [47]- [50], predicting driver behavior [51]- [56], detecting driver, and classification of vehicle types [57]. Representing complicated nonlinear connections between related and dependent variables is the primary benefit of Deep Learning architecture over standard statistical approaches (integrating hierarchical and distributed features) [58].…”

Section: Literature Reviewmentioning

confidence: 99%

Safe Deep Driving Behavior Detection (S3D)

et al. 2022

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The human factor is one of the most critical parameters in car accidents and even traffic occurrences. Driving style affected by human factors comprises driving events (maneuvers) and driver behaviors. Driving event detection is the fundamental step of identifying driving style and facilitates predicting potentially unsafe behaviors, preventing accidents, and imposing restrictions on high-risk drivers. This paper proposes a deep hybrid model to detect safe driver behaviors and driving events using real-time smartphone sensor signals. The ensemble of Multi-layer Perceptron, Support-Vector Machine, and Convolutional Neural Network classifiers process each driving event sample. In order to evaluate our model, we develop an Android Application to capture smartphone sensor signal data. We capture about 24000 driving data from 50 drivers. Results indicate that the fusion model performs better than each individual classifier in terms of Accuracy, False Positive Rate (FPR), and Specificity (96.75, 0.004, and 0.996). This research gives insights to Auto-mobile developers to focus on the speed and cost efficiency of smartphone driver monitoring platforms. Although some insurance and freight management companies utilize smartphones as their monitoring platforms, the market share of these use cases is meager and could improve rapidly with the promotion of new smartphones with better processing and storage.

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“…One advantage of roadside LiDAR is that past information (historical frames) can be used to process the current data [30,31]. With this feature, the accuracy of data processing can be greatly improved.…”

Section: Background Filteringmentioning

confidence: 99%

Vehicle Detection under Adverse Weather from Roadside LiDAR Data

Jian

Tian

et al. 2020

Sensors

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Roadside light detection and ranging (LiDAR) is an emerging traffic data collection device and has recently been deployed in different transportation areas. The current data processing algorithms for roadside LiDAR are usually developed assuming normal weather conditions. Adverse weather conditions, such as windy and snowy conditions, could be challenges for data processing. This paper examines the performance of the state-of-the-art data processing algorithms developed for roadside LiDAR under adverse weather and then composed an improved background filtering and object clustering method in order to process the roadside LiDAR data, which was proven to perform better under windy and snowy weather. The testing results showed that the accuracy of the background filtering and point clustering was greatly improved compared to the state-of-the-art methods. With this new approach, vehicles can be identified with relatively high accuracy under windy and snowy weather.

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Transfer Learning Using Deep Neural Networks for Classification of Truck Body Types Based on Side-Fire Lidar Data

Cited by 29 publications

References 40 publications

A Survey of Vision-Based Transfer Learning in Human Activity Recognition

A Survey of Vision-Based Transfer Learning in Human Activity Recognition

Safe Deep Driving Behavior Detection (S3D)

Vehicle Detection under Adverse Weather from Roadside LiDAR Data

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