Automatic vehicle counting system for traffic monitoring

Crouzil, Alain; Khoudour, Louahdi; Valiere, Paul; Cong, Dung Nghy Truong

doi:10.1117/1.jei.25.5.051207

Cited by 23 publications

(18 citation statements)

References 27 publications

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“…A variety of technologies and analysis methods have been deployed and validated to count pedestrians, bicycles and vehicles crossing bridges and other transportation infrastructure. These methods include in-person observational counting [ 4 , 5 ], motion-activated counters [ 4 ], magnetic inductive loops [ 6 ], infrared light beams, pressure pads, thermal cameras [ 7 ], and digital video [ 8 , 9 ] and imagery analysis [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

Computer vision supported pedestrian tracking: A demonstration on trail bridges in rural Rwanda

Thomas

Gerster

Mugabo³

et al. 2020

PLoS ONE

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Trail bridges can improve access to critical services such as health care, schools, and markets. In order to evaluate the impact of trail bridges in rural Rwanda, it is helpful to objectively know how and when they are being used. In this study, we deployed motion-activated digital cameras across several trail bridges installed by the non-profit Bridges to Prosperity. We conducted and validated manual counting of bridge use to establish a ground truth. We adapted an open source computer vision algorithm to identify and count bridge use reflected in the digital images. We found a reliable correlation with less than 3% error bias of bridge crossings per hour between manual counting and those sites at which the cameras logged short video clips. We applied this algorithm across 186 total days of observation at four sites in fall 2019, and observed a total of 33,800 daily bridge crossings ranging from about 20 to over 1,100 individual uses per day, with no apparent correlation between daily or total weekly rainfall and bridge use, potentially indicating that transportation behaviors, after a bridge is installed, are no longer impacted by rainfall conditions. Higher bridge use was observed in the late afternoons, on market and church days, and roughly equal use of the bridge crossings in each direction. These trends are consistent with the design-intent of these bridges.

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Section: Introductionmentioning

confidence: 99%

Computer vision supported pedestrian tracking: A demonstration on trail bridges in rural Rwanda

Thomas

Gerster

Mugabo³

et al. 2020

PLoS ONE

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“…Intelligent Traffic Flow Analysis (TFA) systems include sensing and data processing techniques that attempt to improve the traditional traffic systems [1]. They integrate additional hardware of sensors, digital traffic signs and cameras and apply advanced techniques to process the data provided by the hardware to intelligently improve vehicles and traffic flow [2]. For example, efficiently adjusting the lights phasing and timing of a traffic light system in such a way that improves the traffic flow and reduce the delay [3].…”

Section: Introductionmentioning

confidence: 99%

Vehicles Counting from Video Stream for Automatic Traffic flow Analysis Systems

Mohd¹

2020

IJETER

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Recently, video-based and real-time vehicle counting become a popular approach for Traffic Flow Analysis (TFA). One of the main objectives of this analysis is to solve the problems that cause traffic congestion including identifying peak hours. Subsequently, this paper proposes an Automatic Video-based Vehicles Counting (AVVC) model for accurate vehicle counting from video streams.The AVCV model includesactive contour which is used to detect whether the object is a vehicle or not, Gaussian distribution which is used for background subtraction and Bilateral Filter which is used for removing shadow and also for smoothing the image. Besides, Kalman Filter is used to reduce the noise in the imagesand Histogram of Oriented Gradient (HOG) and Hough Transform algorithms are used to improve the accuracy of the counting by enabling the model to distinguish between two overlapped objects of vehicles. Hence, our contribution is a strong segmentation algorithm that detects foreground pixels of objects corresponding to moving vehicles.The model is tested and evaluated in terms of counting accuracy and precision using standard dataset video records of three different locations. The AVVC model achieves vehicles counting accuracy of 95.14and precision of92.81% on average.

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“…Following the thriving development of computer vision technology and surveillance equipment in recent years, vision‐based traffic flow surveillance has become one of the key applications of the ITS [1–5]. Previous research has primarily focused on daytime traffic flow surveillance because daytime images are clearer, more image features can be used for vehicle detection; thus, the methods appropriate for daytime vehicle detection [6–15] are more diverse.…”

Section: Introductionmentioning

confidence: 99%

High‐accuracy vehicle lamp detection for real‐time night‐time traffic surveillance

Tsai

Chen

2020

IET intell. transp. syst

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Vehicle lamps are an important image feature of the night‐time vehicle detection algorithm. This study proposes a real‐time night‐time vehicle detection algorithm based on light attenuation characteristic analysis, which consists of vehicle lamp detection and pairing. For the detection phase, this study proposes an automatic dual‐threshold method to quickly extract the attenuation regions around bright objects. This method is highly adaptable and can accurately extract attenuation regions to identify vehicle lamps and strong reflections. For the pairing phase, this study uses the moving trajectories of vehicle lamps to complete preliminary pairing. Then, the vehicle lamp pairing is optimised using the relative vertical and horizontal lamp positions in continuous images. Pixel‐ and object‐based methods were used to verify the experimental vehicle lamp detection results. The results of both verification methods indicate that the proposed algorithm without morphological operation is superior to other algorithms. The accuracy of the pairing phase was >94.6% in scenes with multiple lamps, high‐driving speed, high traffic flow, and rain. Finally, the proposed night‐time vehicle detection algorithm can achieve a real‐time execution speed for images 960 × 720 pixels in size.

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Automatic vehicle counting system for traffic monitoring

Cited by 23 publications

References 27 publications

Computer vision supported pedestrian tracking: A demonstration on trail bridges in rural Rwanda

Computer vision supported pedestrian tracking: A demonstration on trail bridges in rural Rwanda

Vehicles Counting from Video Stream for Automatic Traffic flow Analysis Systems

High‐accuracy vehicle lamp detection for real‐time night‐time traffic surveillance

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