Automatic vehicle counting using background subtraction method on gray scale images and morphology operation

Adi, Kusworo; Widodo, Aris Puji; Widodo, Catur Edi; Pamungkas, Adi; Putranto, Ari Bawono

doi:10.1088/1742-6596/1025/1/012025

Cited by 12 publications

(9 citation statements)

References 7 publications

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“…Another study presented by Li et al provides a YOLO-vocRV model for vehicular detection application, which enables detecting multiple targets of different traffic densities [9]. rough the study evaluation, the authors recognize that the proposed model gives suitable detection rate; however, it gives high false detection rate especially in low training dataset [10]. Sheng et al provide the concept of using R-CNN model to increase the dataset of traffic detection datasets [11].…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Vehicle Detection for Vision-Based Intelligent Transportation Systems Using Convolutional Neural Network Algorithm

Khalifa

Wajdi

Saeed

et al. 2022

Journal of Advanced Transportation

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Vehicle detection in Intelligent Transportation Systems (ITS) is a key factor ensuring road safety, as it is necessary for the monitoring of vehicle flow, illegal vehicle type detection, incident detection, and vehicle speed estimation. Despite the growing popularity in research, it remains a challenging problem that must be solved. Hardware-based solutions such as radars and LIDAR are been proposed but are too expensive to be maintained and produce little valuable information to human operators at traffic monitoring systems. Software based solutions using traditional algorithms such as Histogram of Gradients (HOG) and Gaussian Mixed Model (GMM) are computationally slow and not suitable for real-time traffic detection. Therefore, the paper will review and evaluate different vehicle detection methods. In addition, a method of utilizing Convolutional Neural Network (CNN) is used for the detection of vehicles from roadway camera outputs to apply video processing techniques and extract the desired information. Specifically, the paper utilized the YOLOv5s architecture coupled with k-means algorithm to perform anchor box optimization under different illumination levels. Results from the simulated and evaluated algorithm showed that the proposed model was able to achieve a mAP of 97.8 in the daytime dataset and 95.1 in the nighttime dataset.

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Section: Related Workmentioning

confidence: 99%

“…It is used to classify objects into its attributes or features into K number of clusters. Here, K represents a positive integer value [10,14]. K-means begins with the selection of a single centroid at random [19].…”

Section: Implementation Of Convolutional Neural Network (Cnn)mentioning

confidence: 99%

Vehicle Detection for Vision-Based Intelligent Transportation Systems Using Convolutional Neural Network Algorithm

Khalifa

Wajdi

Saeed

et al. 2022

Journal of Advanced Transportation

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show abstract

“…Moreover, some researcher even combined it with Machine Learning to help improve recognition accuracy [7,8]. We could not ignore the fact that morphology operation is one of the most popular techniques to be used in object recognition [9,10]. The reason for such popularity is that morphology operation while being not very complicated and costly; it could expose the whole structure of an object to ease the recognition of the desired object or to detect an undesired object to be removed from the image.…”

Section: Related Workmentioning

confidence: 99%

Indonesian license plate recognition with improved horizontal-vertical edge projection

Nurhaida

Nududdin

Ramayanti

2021

IJEECS

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<p>License plate recognition (LPR) is one of the classical problems in the field of object recognition. Its application is very crucial in the automation of transportation system since it helps to recognise a vehicle identity, which information is stored in the license plate. LPR usually consists of three major phases: pre-processing, license plate localisation, optical character recognition (OCR). Despite being classical, its implementation faced with much more complicated problems in the real scenario. This paper proposed an improved LPR algorithm based on modified horizontal-vertical edge Projection. The method uses for detecting and localising the region of interest. It is done using the horizontal and vertical projection of the image. Related works proved that the modified horizontal-vertical edge projection is the simplest method to be implemented, yet very effective against Indonesian license plate. However, its performance gets reduced when specular reflection occurs in the sample image. Therefore, morphological operations are utilised in the pre-processing phase to reduce such effects while preserving the needed information. Eighty sample images which captured using various camera configurations were used in this research. Based on the experimental results, our proposed algorithm shows an improvement compared with the previous study and successfully detect 71 license plates in 80 image samples which results in 88.75% accuracy.</p>

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“…The author in [14] reported the dimming is accomplished by adjusting the average current in the LEDs through pulse width modulation of a switch in series with the load. Meanwhile, [15] suggested that a camera with image processing software is used to recognize the target. The color, size, and variant of blinking of the LED markers detected by the camera is the main limiting factor of this method.…”

Section: Introductionmentioning

confidence: 99%

Encoding LED for Unique Markers on Object Recognition System

Tresna¹,

Ahmad²,

Isnaeni³

et al. 2021

IJACSA

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In this paper a new approach of unique markers to detect and track moving objects with the encoding LED marker is presented. In addition, an LED spotlight system that can shoot light in the direction of the target is also proposed. The encoding process is done by making a unique blinking pattern on the LED, and thus the camera and a servomotor as an object recognition system would only recognize a unique marker given by an LED. In this work the camera with OpenCV could capture a unique marker in all variant of blinking patterns. A unique marker is important on object recognition system so the camera could identify the object marked by our unique marker and ignore all other markers that might captured by the camera. In addition, analysis of the PWM signal from an LED is carried out here to determine the characteristics of the LEDs in each color, determine the accuracy of the duty cycle, and the use of the bright-dim method on the LEDs. The results show that the highest accuracy is obtained when a 50% duty cycle is used with the on and off time are set to be 1 second for all LED colors. The benefit of the proposed system is confirmed by implementing an integrated control system as a unique marker. The effectiveness of the blinking LED against other laser interferences is also discussed.

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Automatic vehicle counting using background subtraction method on gray scale images and morphology operation

Cited by 12 publications

References 7 publications

Vehicle Detection for Vision-Based Intelligent Transportation Systems Using Convolutional Neural Network Algorithm

Vehicle Detection for Vision-Based Intelligent Transportation Systems Using Convolutional Neural Network Algorithm

Indonesian license plate recognition with improved horizontal-vertical edge projection

Encoding LED for Unique Markers on Object Recognition System

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