Image-based vehicle analysis using deep neural network: A systematic study

Zhou, Yiren; Nejati, Hossein; Do, Thanh-Toan; Cheung, Ngai-Man; Cheah, Lynette

doi:10.1109/icdsp.2016.7868561

Cited by 84 publications

(35 citation statements)

References 22 publications

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“…The different extraction and segmentation methods produce a set of parameters for each vehicle: Speed, length, width and height of vehicle. The goal of the classification operation is to categorize the vehicle into a number of predefined types using these generated parameters (Fu et al, 2016;Zhou and Cheung, 2016;Wen et al, 2015).…”

Section: Related Workmentioning

confidence: 99%

Real-Time Video Surveillance System for Traffic Management with Background Subtraction Using Codebook Model and Occlusion Handling

Moutakki

Ouloul

Afdel

et al. 2017

Transport and Telecommunication Journal

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The scope of this paper is a video surveillance system constituted of three principal modules, segmentation module, vehicle classification and vehicle counting. The segmentation is based on a background subtraction by using the Codebooks method. This step aims to define the regions of interest associated with vehicles. To classify vehicles in their type, our system uses the histograms of oriented gradient followed by support vector machine. Counting and tracking vehicles will be the last task to be performed. The presence of partial occlusion involves the decrease of the accuracy of vehicle segmentation and classification, which directly impacts the robustness of a video surveillance system. Therefore, a novel method to handle the partial occlusions based on vehicle classification process have developed. The results achieved have shown that the accuracy of vehicle counting and classification exceeds the accuracy measured in some existing systems.

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Section: Related Workmentioning

confidence: 99%

Real-Time Video Surveillance System for Traffic Management with Background Subtraction Using Codebook Model and Occlusion Handling

Moutakki

Ouloul

Afdel

et al. 2017

Transport and Telecommunication Journal

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“…The second concern is the difference between the nature of the tissue image classification, and the image classification task AlexNet originally trained for. Despite this difference in the classification task, previous works such as [14,16,17] have reported the fully connected layers to contain high-level information, seemingly much wider than what is needed for the original classification task. In order to examine this hypothesis and find the best feature set to fulfill our purpose, we assess all three fully connected layers (referred to by f c6, f c7, and f c8 in Figure 1) in AlexNet for their discriminative power in wound tissue classification.…”

Section: Methodsmentioning

confidence: 99%

Fine-Grained Wound Tissue Analysis Using Deep Neural Network

Nejati

Ghazijahani

Abdollahzadeh

et al. 2018

2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Tissue assessment for chronic wounds is the basis of wound grading and selection of treatment approaches. While several image processing approaches have been proposed for automatic wound tissue analysis, there has been a shortcoming in these approaches for clinical practices. In particular, seemingly, all previous approaches have assumed only 3 tissue types in the chronic wounds, while these wounds commonly exhibit 7 distinct tissue types that presence of each one changes the treatment procedure. In this paper, for the first time, we investigate the classification of 7 wound tissue types. We work with wound professionals to build a new database of 7 types of wound tissue. We propose to use pre-trained deep neural networks for feature extraction and classification at the patch-level. We perform experiments to demonstrate that our approach outperforms other state-of-the-art. We will make our database publicly available to facilitate research in wound assessment.

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“…Through deep learning proposed in our paper, the vehicles in the images can be analyzed. The structure of DNN is similar to the one described in [49]. The input and output of DNN is designed according to our work.…”

Section: Deep Learning To Compute the Queue Length Based On Traffic Imentioning

confidence: 99%

An Integrated Algorithm for Intersection Queue Length Estimation Based on IoT in a Mixed Traffic Scenario

et al. 2020

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Featured Application: The proposed algorithm and architecture has the capability of estimating the queue length of waiting vehicles at a signalized intersection, where traffic cameras, edge server, and some connected vehicles are available. This method can resolve the queue length estimation problem in a mixed traffic scenario, especially when there are variable types of vehicles. It can provide key information for traffic lights control and improve the traffic efficiency.Abstract: Nowadays, traffic infrastructures and vehicles are connected through the network benefiting from the development of Internet of Things (IoT). Connected automated cars can provide some useful traffic information. An architecture and algorithm of mobile service computing are proposed for traffic state sensing by integration between IoT and transport system models (TSMs). The formation process of queue at this intersection is analyzed based on the state information of connected vehicles and the velocity of shockwave is calculated to predict queue length. The computing results can be delivered to the traffic information edge server. However, not all the vehicles are capable of connecting to the network and will affect the queue length estimation accuracy. At the same time, traffic cameras transmit the traffic image to the edge server and a deep neuron network (DNN) is constructed on the edge server to tackle the traffic image. It can recognize and classify the vehicles in the image but takes several seconds to work with the complex DNN. At last, the final queue length is determined according to the weight of the two computing results. The integrated result is delivered to the traffic light controller and traffic monitoring center cloud. It reveals that the estimation from DNN can compensate the estimation from shockwave when the penetration rate of connected vehicles is low. A testbed is built based on VISSIM, and the evaluation results demonstrate the availability and accuracy of the integrated queue length estimation algorithm.

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Image-based vehicle analysis using deep neural network: A systematic study

Cited by 84 publications

References 22 publications

Real-Time Video Surveillance System for Traffic Management with Background Subtraction Using Codebook Model and Occlusion Handling

Real-Time Video Surveillance System for Traffic Management with Background Subtraction Using Codebook Model and Occlusion Handling

Fine-Grained Wound Tissue Analysis Using Deep Neural Network

An Integrated Algorithm for Intersection Queue Length Estimation Based on IoT in a Mixed Traffic Scenario

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