Video Based Vehicle Detection and Counting Using Digital Image Processing

Abstract: Vehicle detection and counting is very much important for the purpose of upgrading and widening the road. The information obtained from the traffic monitoring can be used in planning the budget for road maintenance. The traffic monitoring can be done automatically or by detecting and counting the vehicles manually using human labors. In manual method of traffic monitoring the person records the data using tally sheet which may leads to the human errors and most of the automatic traffic census system used nowadays focuses on detecting and counting the vehicles by using devices called magnetic loop detectors. These devices are costly and once installed, cannot be removed. So, it is necessary to build the system that is capable of detecting and counting vehicles without involving persons for traffic monitoring and costlier devices to detect and count the vehicles. For that purpose in this work simple cameras are used for detection and counting of vehicles. Keywords: Detection, Counting, Background subtraction, Canny edge detection, Kalman filter.

Download Full-text

Vehicle Detection In Remote Sensing Images

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.k1807.0981119 ◽

2019 ◽

Vol 8 (11) ◽

pp. 928-933

Keyword(s):

Feature Extraction ◽

Vehicle Detection ◽

Low Complexity ◽

Traffic Monitoring ◽

Color Spaces ◽

Continuous Increase ◽

The Road ◽

Uav Images ◽

Candidate Regions ◽

Detection And Localization

Traffic monitoring and management is one of the most crucial tasks of governing bodies in modern big cities. With each passing day the traffic problem grows in complexity due to the continuous increase of participating vehicles and the hard expansion of the road network and parking places. In this article we introduce a new method for vehicle detection and localization in parking lots using high resolution UAV images. In order to end up with practical and yet effective approach, which could be implemented on low computing hardware resources and integrated with the camera in the UAV, we considered simple steps in the proposed algorithm for optimization. It follows the machine learning pipeline such as preprocessing, sensing, feature extraction, training and classification. In preprocessing the images are thresholded iteratively in multiple color spaces to extract the candidate regions of interest (ROI). The algorithm relies on point and shape features using fast techniques in the feature extraction. The features are then clustered by the K-means algorithm and represented by the resulted clusters’ centers. Region based linear classification is finally applied using SVM to classify if the object is a vehicle or else. The proposed approach proved high detection and classification accuracy more than 86% and still running under the low complexity constraint..

Download Full-text

Sealing The Cracks in the Wear Layer of the Road Surface by Friction against a Rotary Active Element

International Journal of Modeling and Optimization ◽

10.7763/ijmo.2022.v12.795 ◽

2022 ◽

pp. 26-30

Author(s):

Stefan Ionita ◽

Stefan Velicu

Keyword(s):

Experimental Analysis ◽

Active Element ◽

Intelligent System ◽

Traffic Monitoring ◽

Road Maintenance ◽

Maintenance Costs ◽

The Road ◽

Low Costs ◽

Time Required ◽

Recycled Waste

The main objective of the research paper is the theoretical and experimental analysis of the method proposed for sealing (clogging) cracks in asphalt, by means of a cylindrical bitumen bar, enriched with plastic and rubber granules (obtained from the use of waste), which melts and infuses into the cracked zone by rotation and friction against it. After analyzing the technical characteristics of the sealed area and the time required to apply the bitumen layer, this method can be chosen in the future to the detriment of the expensive operations of partial milling of the cracked wear layer, making possible the repair of cracks by sealing(clogging), using the friction procedure. The research results highlighted the diminution of road maintenance costs using the method of friction, the decrease of cracks repair time, maintaining the initial characteristics of the repaired area, incorporating a waterproofing material (plastic and rubbber granules from recycled waste), keeping the wear layer in good conditions, possibility of embedding an intelligent system of traffic monitoring at low costs etc.

Download Full-text

Detection and Counting of Red Blood Cells in Human Urine using Canny Edge Detection and Circle Hough Transform Algorithms

2019 IEEE 11th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management ( HNICEM ) ◽

10.1109/hnicem48295.2019.9072708 ◽

2019 ◽

Author(s):

Meo Vincent Caya ◽

Dionis Padilla ◽

Gilbert Ombay ◽

Arnold Janssen Hernandez

Keyword(s):

Edge Detection ◽

Red Blood Cells ◽

Human Urine ◽

Hough Transform ◽

Blood Cells ◽

Canny Edge Detection ◽

Detection And Counting ◽

Canny Edge

Download Full-text

Trafic Signs Detection Based On Saliency Map Using Canny Edge

Journal of Electrical Electronics and Informatics ◽

10.24843/jeei.2018.v02.i01.p01 ◽

2018 ◽

Vol 2 (1) ◽

pp. 1

Author(s):

Putri Alit Widyastuti Santiary ◽

I Made Oka Widyantara ◽

Rukmi Sari Hartati

Keyword(s):

Edge Detection ◽

Euclidean Distance ◽

Exact Result ◽

Saliency Map ◽

Traffic Sign ◽

Canny Edge Detection ◽

Pixel Intensity ◽

The Road ◽

Road Sign ◽

Canny Edge

This paper proposed Canny edge detection to detected saliency map on traffic sign. The edge detection functions by identifying the bounds from an object on an image. The edge of an image is an area that has a strong intensity of light.The pixel intensity of an image changes from low to high values or otherwise. Detecting the edge of an image significantly will decrease the amount of data and filters insignificant information by not deleting necessary structure from the image. The image used for this paper is a digital capture of a traffic sign with a background. The result of this study shows that Canny edge detection creates saliency map from the traffic sign and separates the road sign from the background. The image result tested by calculating the saliency distance between a tested image and trained image using normalized Euclidean distance. The value ofnormalized Euclidean distance is set between 0 to 2. The testing process is done by calculating the nearest distance between the tested vector features and trained vector features. From the examination as a whole, it can be concluded that road sign detection using saliency map model can be built by Canny edge detection. From the whole system examination, it resulted a accuracy value of 0,65. This value shows that the data was correctly classified by 65%. The precision value has an outcome of 0,64, shows that the exact result of the classification is 64%. The recall value has an outcome of 0,94. This value shows that the success rate of recognizing a data from the whole data is 94%.

Download Full-text

Research on Vehicle Detection and Shadow Elimination Based on Static Image Sequence

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.602-605.2362 ◽

2014 ◽

Vol 602-605 ◽

pp. 2362-2365

Author(s):

Quan Wu Li ◽

Yu Hui Li ◽

Bo Li ◽

Yi Chen

Keyword(s):

Edge Detection ◽

Background Subtraction ◽

Vehicle Detection ◽

Image Sequence ◽

Background Modeling ◽

High Definition ◽

Canny Edge Detection ◽

New Approach ◽

Shadow Elimination ◽

Canny Edge

Focused on static high-definition sequence images captured on the highway bayonet, this paper proposes a new approach for vehicle detection and shadow elimination based on average background modeling, which uses average background model and background subtraction to locate vehicle roughly, eliminates shadow of the vehicle using canny edge detection with dynamic histogram threshold determined by the histogram of the image. Experiments show that this method can locate the position of vehicle quickly and accurately.

Download Full-text

Car Detection from Low-Altitude UAV Imagery with the Faster R-CNN

Journal of Advanced Transportation ◽

10.1155/2017/2823617 ◽

2017 ◽

Vol 2017 ◽

pp. 1-10 ◽

Cited By ~ 29

Author(s):

Yongzheng Xu ◽

Guizhen Yu ◽

Yunpeng Wang ◽

Xinkai Wu ◽

Yalong Ma

Keyword(s):

Vehicle Detection ◽

Traffic Monitoring ◽

Future Research ◽

Traffic Sensors ◽

Loop Detectors ◽

Car Detection ◽

Parking Lot ◽

Low Altitude ◽

Uav Images ◽

Detection Speed

UAV based traffic monitoring holds distinct advantages over traditional traffic sensors, such as loop detectors, as UAVs have higher mobility, wider field of view, and less impact on the observed traffic. For traffic monitoring from UAV images, the essential but challenging task is vehicle detection. This paper extends the framework of Faster R-CNN for car detection from low-altitude UAV imagery captured over signalized intersections. Experimental results show that Faster R-CNN can achieve promising car detection results compared with other methods. Our tests further demonstrate that Faster R-CNN is robust to illumination changes and cars’ in-plane rotation. Besides, the detection speed of Faster R-CNN is insensitive to the detection load, that is, the number of detected cars in a frame; therefore, the detection speed is almost constant for each frame. In addition, our tests show that Faster R-CNN holds great potential for parking lot car detection. This paper tries to guide the readers to choose the best vehicle detection framework according to their applications. Future research will be focusing on expanding the current framework to detect other transportation modes such as buses, trucks, motorcycles, and bicycles.

Download Full-text