An automatic traffic density estimation using Single Shot Detection (SSD) and MobileNet-SSD

Biswas, Debojit; Su, Hongbo; Wang, Chengyi; Stevanovic, Aleksandar; Wang, Weimin

doi:10.1016/j.pce.2018.12.001

Cited by 96 publications

(49 citation statements)

References 8 publications

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“…5 SSD: Different scales of feature mapping is extracted from the output of different layers. Divide the meshes into different scales and conclude the object categories in the grids [9].…”

Section: Baseline Methodsmentioning

confidence: 99%

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A Novel Object Detection Algorithm in Video

2019

Proceedings of 2019 the 9th International Workshop on Computer Science and Engineering

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Deep learning technology performs effect in feature extraction of images. Nowadays, with the development of video monitoring, the application of deep learning technology to surveillance video has profound implications. The effects of traditional video recognition are not satisfactory, but deep learning methods perform effect in many scenes of image classification. This paper proposed a novel object detection algorithm in video. It combined the traditional methods of extracting feature and deep learning algorithm to realize vehicle identification based on surveillance video. The method used the frame difference method and background subtraction to preprocess the image, and then trained a network model based on YOLO to perform object detection and obtain the categories and location information of the monitored vehicle. Compared with the existing object detection algorithms faster RNN, our method can achieve higher accuracy and can significantly shorten the time for detection, which can recognize the object of vehicle video quickly and efficiently. The method can meet the requirements of real-time detection.

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“…5 SSD: Different scales of feature mapping is extracted from the output of different layers. Divide the meshes into different scales and conclude the object categories in the grids [9].…”

Section: Baseline Methodsmentioning

confidence: 99%

“…The end-to-end method is based on non-regional candidate, the two representative algorithms are the YOLO [8] and SSD [9]. They obtain candidate regions of the image by means of uniform segmentation operation.…”

Section: Background and Related Workmentioning

confidence: 99%

A Novel Object Detection Algorithm in Video

2019

Proceedings of 2019 the 9th International Workshop on Computer Science and Engineering

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“…The whole process only takes one step, so it has the advantage of fast speed. However, an important disadvantage of uniform dense sampling is that training is difficult, mainly because the positive sample and the negative sample (background) are extremely unbalanced, resulting in slightly lower accuracy of the model [22].…”

Section: Ssdmentioning

confidence: 99%

Design and Analysis of Refined Inspection of Field Conditions of Oilfield Pumping Wells Based on Rotorcraft UAV Technology

Zhou

Wu³

et al. 2019

Electronics

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The traditional oil well monitoring method relies on manual acquisition and various high-precision sensors. Using the indicator diagram to judge the working condition of the well is not only difficult to establish but also consumes huge manpower and financial resources. This paper proposes the use of computer vision in the detection of working conditions in oil extraction. Combined with the advantages of an unmanned aerial vehicle (UAV), UAV aerial photography images are used to realize real-time detection of on-site working conditions by real-time tracking of the working status of the head working and other related parts of the pumping unit. Considering the real-time performance of working condition detection, this paper proposes a framework that combines You only look once version 3 (YOLOv3) and a sort algorithm to complete multi-target tracking in the form of tracking by detection. The quality of the target detection in the framework is the key factor affecting the tracking effect. The experimental results show that a good detector makes the tracking speed achieve the real-time effect and provides help for the real-time detection of the working condition, which has a strong practical application.

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“…This section presents various spatial analysis tools integrated with ArcGIS that can identify the hotspots in the area of interest.Identification ofhotspots is the most important aspect in controlling traffic accidents because it enables effective traffic management by optimization ofroad signs and traffic police personnel, and deployment of automatic traffic monitoring systems at the hotspots [40], [41]. The accident incidentslocated on a geographical space are studied to identify any systemic pattern of occurrence of crashes to determine whether the accident locations are distributed randomly or as a clustered pattern.…”

Section: Spatial Analysis Techniquesmentioning

confidence: 99%

Identification of Traffic Accident Hotspots using Geographical Information System (GIS)

Srikanth¹,

Srikanth²,

Arockiasamy³

2019

IJEAT

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Limiting the number andseverity of traffic accidents is one of the major goals of road traffic safety management.The alarming rate of road accidents globally emphasizes the importance of an effective traffic safety management system. Identification of accident hotspots is the first step towards implementation of efficient traffic safety management.Until the arrival of Geographical Information System (GIS),traffic accident analyses have been performed based ontraditional statistical methods alone. The advent of GIS-based techniques has led toimproved traffic accident analysis by employing spatial statistics,enabling engineers and researchers to account for variation in the spatial characteristics of hotspot locations in the analysis. This paper discusses the different spatial and statistical methods that are employedintraffic accident hotspots identification. An example application of Planar Kernel Density Estimation (PKDE)for hotspot identification is presented based on crash data for Des Moines city of Iowa state. The effect of varying bandwidths in creating density mapsis investigated and the optimum bandwidth to obtain distinct hotspots is identified as 500 m for the chosen study area.The paper also discusses the scope for future research in traffic accident hotspot analysis.

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An automatic traffic density estimation using Single Shot Detection (SSD) and MobileNet-SSD

Cited by 96 publications

References 8 publications

A Novel Object Detection Algorithm in Video

A Novel Object Detection Algorithm in Video

Design and Analysis of Refined Inspection of Field Conditions of Oilfield Pumping Wells Based on Rotorcraft UAV Technology

Identification of Traffic Accident Hotspots using Geographical Information System (GIS)

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