The Traffic Congestion Investigating System by Image Processing from CCTV Camera

Eamthanakul, Busarin; Ketcham, Mahasak; Chumuang, Narumol

doi:10.1109/icdamt.2017.7904969

Cited by 32 publications

(14 citation statements)

References 12 publications

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“…The successive pixels values in a current image frame are subtracted from the next image frame and give the foreground and background areas in white and black respectively. A threshold value is set to distinguish between the foreground object from the background [35][36][37][38][39][40][41]. Block sparse RPCA algorithm and low rank representation with UAV have been used in order to count the number of vehicles [42] but the drawback of this work is that UAV takes static images only.…”

Section: Related Workmentioning

confidence: 99%

Traffic Congestion Avoidance System Using Foreground Estimation and Cascade Classifier

et al. 2020

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In recent decades, the traffic on road increased in a huge number. It is very important to manage the safety of the humans as well as to make an efficient flow of the traffic. To manage the traffic flow and to overcome from the situation of the traffic congestion the vehicle detection and counting needs a greater amount of accuracy. In this work, two different techniques are proposed that provides better performance in terms of F-Measure score and Error Ratio. The first technique is based on the foreground estimation while the second proposed technique is based on the training of dataset using a cascade classifier which is based on the Histogram of Oriented Gradients (HOG). Furthermore, four images are provided at once to the proposed system to count the vehicles and generate a signal that shows a greater number of vehicles in that image. The priority of each image will be set on the basics of greater number of vehicles present. The proposed techniques showed outstanding performance on a sunny and a cloudy day which is verified from the experimental results.

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Section: Related Workmentioning

confidence: 99%

Traffic Congestion Avoidance System Using Foreground Estimation and Cascade Classifier

et al. 2020

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“…The Total Vehicles feature is the number of bounding boxes provided by the vehicle detector and evaluated for each video frame [10,13,20,21,24,38,39,40,41,42,43].…”

Section: Methodsmentioning

confidence: 99%

Vehicular Traffic Congestion Classification by Visual Features and Deep Learning Approaches: A Comparison

Impedovo

Balducci

Dentamaro

et al. 2019

Sensors

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Automatic traffic flow classification is useful to reveal road congestions and accidents. Nowadays, roads and highways are equipped with a huge amount of surveillance cameras, which can be used for real-time vehicle identification, and thus providing traffic flow estimation. This research provides a comparative analysis of state-of-the-art object detectors, visual features, and classification models useful to implement traffic state estimations. More specifically, three different object detectors are compared to identify vehicles. Four machine learning techniques are successively employed to explore five visual features for classification aims. These classic machine learning approaches are compared with the deep learning techniques. This research demonstrates that, when methods and resources are properly implemented and tested, results are very encouraging for both methods, but the deep learning method is the most accurately performing one reaching an accuracy of 99.9% for binary traffic state classification and 98.6% for multiclass classification.

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“…The vehicle detection and counting algorithm was implemented and also tested on an embedded platform of smart cameras. Eamthanakul et al [4] designed a Traffic Congestion Investigating System by Image Processing from closed circuit television (CCTV) Camera to check a traffic condition from a traffic image on the road. Then, a user can check in advance for a traffic condition in a specific time.…”

Section: Related Workmentioning

confidence: 99%

“…Traffic monitoring systems have become more intelligent than ever before. Traffic signaling systems are adaptive, car counting is automated [1,2], and car density estimation on the road is also becoming automated [3,4]. Every day, new technologies are introduced into this field to make intelligent traffic monitoring systems (ITMS) better.…”

Section: Introductionmentioning

confidence: 99%

Speed Estimation of Multiple Moving Objects from a Moving UAV Platform

Biswas

Wang

et al. 2019

IJGI

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Speed detection of a moving object using an optical camera has always been an important subject to study in computer vision. This is one of the key components to address in many application areas, such as transportation systems, military and naval applications, and robotics. In this study, we implemented a speed detection system for multiple moving objects on the ground from a moving platform in the air. A detect-and-track approach is used for primary tracking of the objects. Faster R-CNN (region-based convolutional neural network) is applied to detect the objects, and a discriminative correlation filter with CSRT (channel and spatial reliability tracking) is used for tracking. Feature-based image alignment (FBIA) is done for each frame to get the proper object location. In addition, SSIM (structural similarity index measurement) is performed to check how similar the current frame is with respect to the object detection frame. This measurement is necessary because the platform is moving, and new objects may be captured in a new frame. We achieved a speed accuracy of 96.80% with our framework with respect to the real speed of the objects.

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The Traffic Congestion Investigating System by Image Processing from CCTV Camera

Cited by 32 publications

References 12 publications

Traffic Congestion Avoidance System Using Foreground Estimation and Cascade Classifier

Traffic Congestion Avoidance System Using Foreground Estimation and Cascade Classifier

Vehicular Traffic Congestion Classification by Visual Features and Deep Learning Approaches: A Comparison

Speed Estimation of Multiple Moving Objects from a Moving UAV Platform

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