786 Hits in 7.9 sec

Real-Time Vehicle Classification and Tracking Using a Transfer Learning-Improved Deep Learning Network

Bipul Neupane, Teerayut Horanont, Jagannath Aryal
2022 Sensors  
To tackle P2, we trained and applied transfer learning-based fine-tuning on several state-of-the-art YOLO (You Only Look Once) networks.  ...  Deep learning (DL) and computer vision are intelligent methods; however, accurate real-time classification and tracking come with problems.  ...  Seven classes of vehicles, including car, bus, taxi, bike, pickup, truck, and trailer, were manually annotated for our purpose.  ... 
doi:10.3390/s22103813 pmid:35632222 pmcid:PMC9144024 fatcat:fntflnnvnzgtlbld6ykou4jice

DeepWiTraffic: Low Cost WiFi-Based Traffic Monitoring System Using Deep Learning [article]

Myounggyu Won, Sayan Sahu, Kyung-Joon Park
2020 arXiv   pre-print
One of the biggest challenges is, however, the high cost especially in covering the huge rural road network.  ...  , SUVs, pickup trucks, and large trucks.  ...  , (2) algorithms to extract the "induced" CSI amplitude and phase values corresponding to a passing vehicle, and (3) design of a deep learning network for effective vehicle classification.  ... 
arXiv:1812.08208v2 fatcat:ft5rv6n6ijb77n6rm2ergw3muy

Intelligent Traffic-Monitoring System Based on YOLO and Convolutional Fuzzy Neural Networks

Cheng-Jian Lin, Jyun-Yu Jhang
2022 IEEE Access  
On the GRAM-RTM data set, the mean average precision and F-measure (F1) of the proposed YOLO-CFNN and YOLO-VCFNN vehicle classification methods are 99%, superior to those of other methods.  ...  On actual roads in Taiwan, the proposed YOLO-CFNN and YOLO-VCFNN methods not only have a high F1 score for vehicle classification but also have outstanding accuracy in vehicle counting.  ...  In this study, an FNN was embedded into a deep learning network to reduce the number of parameters used in the network and obtain superior classification results.  ... 
doi:10.1109/access.2022.3147866 fatcat:7tecq263ibaddftss36inxnpaq

Machine Learning-Based Highway Truck Commodity Classification Using Logo Data

Pan He, Aotian Wu, Xiaohui Huang, Anand Rangarajan, Sanjay Ranka
2022 Applied Sciences  
For the former, we leverage state-of-the-art deep-learning-based text recognition algorithms on images.  ...  This, along with prior work on trailer classification, can be effectively used for automatically deriving commodity types for trucks moving on highways.  ...  The deep metric learning model was implemented in PyTorch. We used the Inception network [38] as the backbone with a global pooling layer and a fully connected layer added on top of it.  ... 
doi:10.3390/app12042075 fatcat:fwoccn5divbxbgnggmvndmyk7m

Automatic Vehicle Identification and Classification Model Using the YOLOv3 Algorithm for a Toll Management System

Sudhir Kumar Rajput, Jagdish Chandra Patni, Sultan S. Alshamrani, Vaibhav Chaudhari, Ankur Dumka, Rajesh Singh, Mamoon Rashid, Anita Gehlot, Ahmed Saeed AlGhamdi
2022 Sustainability  
neural network and deep learning.  ...  In this research, we used the deep learning YOLOv3 algorithm and trained it on a custom dataset of vehicles that included different vehicle classes as per the Indian Government's recommendation to implement  ...  The advanced method of implementing AVI and AVC uses machine learning, neural networks and deep learning.  ... 
doi:10.3390/su14159163 fatcat:j7hdxwoclzbjfkvcrxzhiiaxsm

Spectral features for audio based vehicle and engine classification

Alicja Wieczorkowska, Elżbieta Kubera, Tomasz Słowik, Krzysztof Skrzypiec
2017 Journal of Intelligent Information Systems  
The experiments were performed first on on-road recordings, and then continued with test bench (dyno) recordings.  ...  In this paper we address automatic vehicle and engine identification based on audio information.  ...  Acknowledgments This work was partially supported by the Research Center of PJAIT, supported by the Ministry of Science and Higher Education in Poland.  ... 
doi:10.1007/s10844-017-0459-2 fatcat:2kc6etqs3ve7bemc33drph64g4

The Channel as a Traffic Sensor: Vehicle Detection and Classification based on Radio Fingerprinting [article]

Benjamin Sliwa and Niko Piatkowski and Christian Wietfeld
2020 arXiv   pre-print
Ubiquitously deployed Internet of Things (IoT)- based automatic vehicle classification systems will catalyze data-driven traffic flow optimization in future smart cities and will transform the road infrastructure  ...  a binary classification success ratio of more than 99% and an overall accuracy of 93.83% for a classification task with seven different classes.  ...  Increasing the road capacity in straightforward ways -e.g., through construction of new lanes and roads -is often not possible due to the involved costs and spatial limitations (especially in inner city  ... 
arXiv:2003.09827v1 fatcat:q3rn4z3vjrfp5cv3uv7pkocoji

Scanning the Issue

Azim Eskandarian
2021 IEEE transactions on intelligent transportation systems (Print)  
The current focus of ATFM is generally on optimally utilizing the available airspace and airport capacities while maintaining the required safety separation between aircraft.  ...  To this end, the authors propose an ATFM framework for scrutinizing the stochastic nature of ATS through a chance-constraint-based probabilistic approach.  ...  STNN first develops two spatial models into LSTM as the encoder to learn the dynamic spatio-temporal dependencies from three perspectives of links, regions, and road networks.  ... 
doi:10.1109/tits.2021.3128609 fatcat:ntohivivqvdspec2nfokzaer6a

Estimation of Lane-Level Traffic Flow Using a Deep Learning Technique

Chieh-Min Liu, Jyh-Ching Juang
2021 Applied Sciences  
This paper proposes a neural network that fuses the data received from a camera system on a gantry to detect moving objects and calculate the relative position and velocity of the vehicles traveling on  ...  The number of vehicles passing on the freeway was then calculated by drawing virtual lines and hot zones. The velocity of each vehicle was also recorded.  ...  A deep learning procedure using a neural network technique was designed for estimating the velocity of vehicles.  ... 
doi:10.3390/app11125619 fatcat:ssttitznxrcb7pbn6l3egqdrce

Moviescope: Large-scale Analysis of Movies using Multiple Modalities [article]

Paola Cascante-Bonilla, Kalpathy Sitaraman, Mengjia Luo, Vicente Ordonez
2019 arXiv   pre-print
We demonstrate the usefulness of content-based methods in this domain in contrast to human-based and metadata-based predictions in the era of deep learning.  ...  To this end, we also introduce Moviescope, a new large-scale dataset of 5,000 movies with corresponding movie trailers (video + audio), movie posters (images), movie plots (text), and metadata.  ...  For video, we additionally performed experiments using spatio-temporal feature learning using deep three-dimensional convolutional networks (C3D) [37] , and the Two-Stream Inflated 3D convolutional network  ... 
arXiv:1908.03180v1 fatcat:vnuwwb3gijehpofa2wdbla2whi

Urban Traffic Monitoring and Analysis Using Unmanned Aerial Vehicles (UAVs): A Systematic Literature Review

Eugen Valentin Butilă, Răzvan Gabriel Boboc
2022 Remote Sensing  
It can be stated that this is still a field in its infancy and that progress in advanced image processing techniques and technologies used in the construction of UAVs will lead to an explosion in the number  ...  of applications, which will result in increased benefits for society, reducing unpleasant situations, such as congestion and collisions in major urban centers of the world.  ...  remote sensing [58] , deep learning approaches for road extraction [59] , and advances toward future transportation.  ... 
doi:10.3390/rs14030620 fatcat:qjtkacwm2rfhfmsrn3ju57tl3i

Segment-based CO2 Emission Evaluations from Passenger Cars based on Deep Learning Techniques

Naghmeh Niroomand, Christian Bach, Miriam Elser
2021 IEEE Access  
To analyze the root of this problem and evaluate potential solutions, this paper applies deep learning techniques to evaluate the inter-class (namely micro, small, middle, upper middle, large and luxury  ...  The overall level of emissions from the Swiss passenger cars is strongly dependent on the fleet composition.  ...  ACKNOWLEDGMENT The authors thank the Federal Roads Office (FEDRO) for providing the Swiss Vehicle Information System (MOFIS) data and the vehicle technical dataset and the Vehicle Expert Partner (Au-to-i-dat  ... 
doi:10.1109/access.2021.3135604 fatcat:yycl64qhy5hlpbe6twv2sd2qqe

A VR Truck Docking Simulator Platform for Developing Personalized Driver Assistance

Pedro Ribeiro, André Frank Krause, Phillipp Meesters, Karel Kural, Jason van Kolfschoten, Marc-André Büchner, Jens Ohlmann, Christian Ressel, Jan Benders, Kai Essig
2021 Applied Sciences  
The paper reports the usage of VISTA-Sim through the scenario of parking a semi-trailer truck in a loading bay, demonstrating how to learn from driver behaviours.  ...  The evolution of this platform can offer ideal conditions for the development of ADAS systems that can automatically and continuously learn from and adapt to an individual driver.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/app11198911 fatcat:tywm2ppqqzg3rimzpwkrf7zace

Predicting Hazardous Driving Events Using Multi-Modal Deep Learning Based on Video Motion Profile and Kinematics Data

Z. Gao, Y. Liu, J. Y. Zheng, R. Yu, X. Wang, P. Sun
2018 2018 21st International Conference on Intelligent Transportation Systems (ITSC)  
In this study, we develop a model based on a low-definition driving record instrument and the vehicle kinematic data for postaccident analysis by multi-modal deep learning method.  ...  The analysis results indicate that the proposed multi-modal deep learning model can identify hazardous events within a large volumes of data at an AUC of 0.81, which outperforms the stateof-the-art random  ...  Engineering Laboratory for Integrated Optimization of Road Traffic and Safety Analysis Technologies, and Chinese 111 Project (B17032).  ... 
doi:10.1109/itsc.2018.8569659 dblp:conf/itsc/GaoLZYWS18 fatcat:amv4u5gtvnacrcr4vqjuowaehi

Decarbonizing China's Road Transport Sector: Strategies toward Carbon Neutrality

Lulu Xue, Daizong Liu
2022 World Resources Institute  
This study aims to inform China road transport sector's emission reduction target, identification of cost-effective measures that deliver on the sectoral emission reduction targets, facilitate low-carbon  ...  investments, and identification of decarbonization measures with air pollution reduction co-benefits.  ...  The German Federal Ministry for Economic Affairs and Climate Action supports this initiative on the basis of a decision adopted by the Bundestag.  ... 
doi:10.46830/wrirpt.21.00145 fatcat:ae5xluo3ubd5hoaoixlyedw47a
« Previous Showing results 1 — 15 out of 786 results