Vehicle detection and recognition for intelligent traffic surveillance system

Tang, Yonghong; Cong-zhe, Zhang; Gu, Renshu; Li, Peng; Yang, Bin

doi:10.1007/s11042-015-2520-x

Cited by 161 publications

(70 citation statements)

References 25 publications

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“…Panchal et al used Harris corner and connected component-based method to segment the characters [7]. Haar-like features and AdaBoost algorithms were applied; feature extraction was reported in another study [8]. Gabor wavelet transform and local binary operator were also used.…”

Section: Literature Reviewmentioning

confidence: 99%

Automatic Number Plate Recognition Using Random Forest Classifier

Akhtar

Ali

2020

SN COMPUT. SCI.

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Automatic number plate recognition system is a mass surveillance embedded system that recognizes the number plate of the vehicle. This system is generally used for traffic management applications. It should be very efficient in detecting the number plate in noisy as well as in low illumination and also within required time frame. This paper proposes a number plate recognition method by processing vehicle's rear or front image. After image is captured, processing is divided into four steps which are preprocessing, number plate localization, character segmentation and character recognition. Preprocessing enhances the image for further processing, number plate localization extracts the number plate region from the image, character segmentation separates the individual characters from the extracted number plate, and character recognition identifies the optical characters by using random forest classification algorithm. Experimental results reveal that the accuracy of this method is 90.9%.

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Section: Literature Reviewmentioning

confidence: 99%

Automatic Number Plate Recognition Using Random Forest Classifier

Akhtar

Ali

2020

SN COMPUT. SCI.

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“…The author uses automatic monitoring digital cameras to take snapshots of moving motion pictures [1]. From the collected images, sequences are extracted to represent features of a vehicle.…”

Section: Yong Tang Et Al Present Vehicle Detection and Recognitionmentioning

confidence: 99%

Trajectory Object Detection using Deep Learning Algorithms

Elavarasi*¹,

Jayanthi²,

Basker³

2019

IJRTE

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Video surveillance data in smart cities needs to analyze a large amount of video footage in order to locate the people who are violating the traffic rules. The fact is that it is very easy for the human being to recognize different objects in images and videos. For a computer program this is quite a difficult task. Hence there is a need for visual big data analytics which involves processing and analyzing large scale visual data such as images or videos. One major application of trajectory object detection is the Intelligent Transport Systems (ITS). Vehicle type detection, tracking and classification play an important role in ITS. In order to analyze huge amount of video footage deep learning algorithms have been deployed. The main phase of vehicle type detection includes annotating the data, training the model and validating the model. The problems and challenges in identifying or detecting type of vehicle are due to weather, shadows, blurring effect, light condition and quality of the data. In this paper deep learning algorithms such as Faster R CNN and Mask R CNN and Frameworks like YOLO were used for the object detection. Dataset (different types of vehicle pictures in video format) were collected both from in-house premises as well as from the Internet to detect and recognize the type of vehicles which are common in traffic systems. The experimental results show that among the three approaches used the Mask R CNN algorithm is found to be more efficient and accurate in vehicle type detection.

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“…A typical system configuration uses edge platforms for sensing visual data, which is transmitted to and processed by the host with a DNN inference engine. This configuration is appealing in the applications where the host make central decision and control, such as vehicle detection and recognition [1], remote monitoring [2], and scene analysis [3]. Although this approach relieves the inference demand for the edge platforms, its performance will largely depend on reliable transmission of the images through a wireless channel with limited bandwidth.…”

Section: Related Workmentioning

confidence: 99%

Edge-Host Partitioning of Deep Neural Networks with Feature Space Encoding for Resource-Constrained Internet-of-Things Platforms

Amir

et al. 2018

2018 15th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)

126

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This paper introduces partitioning an inference task of a deep neural network between an edge and a host platform in the IoT environment. We present a DNN as an encoding pipeline, and propose to transmit the output feature space of an intermediate layer to the host. The lossless or lossy encoding of the feature space is proposed to enhance the maximum input rate supported by the edge platform and/or reduce the energy of the edge platform. Simulation results show that partitioning a DNN at the end of convolutional (feature extraction) layers coupled with feature space encoding enables significant improvement in the energy-efficiency and throughput over the baseline configurations that perform the entire inference at the edge or at the host.

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Vehicle detection and recognition for intelligent traffic surveillance system

Cited by 161 publications

References 25 publications

Automatic Number Plate Recognition Using Random Forest Classifier

Automatic Number Plate Recognition Using Random Forest Classifier

Trajectory Object Detection using Deep Learning Algorithms

Edge-Host Partitioning of Deep Neural Networks with Feature Space Encoding for Resource-Constrained Internet-of-Things Platforms

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