Vehicle detection and counting using haar feature-based classifier

Choudhury, Soumyadeep; Chattopadhyay, Soummyo Priyo; Hazra, Tapan Kumar

doi:10.1109/iemecon.2017.8079571

Cited by 51 publications

(16 citation statements)

References 9 publications

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“…To illuminate influence of the represented clustering methods on the pattern recognition quality the authors implemented Haar [23] cascade classifier on the given example to detect the car. It is possible to see the results in Fig.…”

Section: Methodsmentioning

confidence: 99%

Image Clustering Method based on Particle Swarm Optimization

Kim

Matveeva

Viksnin

et al. 2018

Annals of Computer Science and Information Systems

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To implement efficient computer vision mechanisms, efficient image clustering methods are important. The paper elaborates a clustering method based on particle swarm optimization (PSO) which provides automatic establishment of clustering parameters. The developed PSO based clustering method was tested on 860 images for a car vision system and its results and contribution to the pattern recognition quality improvement were assessed in comparison with fuzzy C-means and k-means. The results do not differ significantly, but distinction in average time of work for these methods was noted. The PSO clustering method is faster than k-means and slower than fuzzy C-means. However, fuzzy C-means method does not guarantee correct results during the further analysis, so the PSO clustering method can be more efficient for implementation in computer vision systems.

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Section: Methodsmentioning

confidence: 99%

Image Clustering Method based on Particle Swarm Optimization

Kim

Matveeva

Viksnin

et al. 2018

Annals of Computer Science and Information Systems

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“…The Haar highlight-based arrangement is quicker and equipped for identifying vehicles at a quickly evolving condition. Trial results demonstrate that the precision is above 90% [5].…”

Section: Introductionmentioning

confidence: 94%

Video Data Extraction and Processing for Investigation of Vehicles’ Impact on the Asphalt Deformation Through the Prism of Computational Algorithms

Vrtagic¹,

Softić²,

Ponjavić³

et al. 2020

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There are numerous algorithms and solutions for car or object detection as humanity is aiming towards the smart city solutions. Most solutions are based on counting, speed detection, traffic accidents and vehicle classification. The mentioned solutions are mostly based on high-quality videos, wide angles camera view, vehicles in motion, and are optimized for good visibility conditions intervals. A novelty of the proposed algorithm and solution is more accurate digital data extraction from video file sources generated by security cameras in Bosnia and Herzegovina from M18 roadway, but not limited only to that particular source. From the video file sources, data regarding number of vehicles, speed, traveling direction, and time intervals for the region of interest will be collected. Since finding contours approach is effective only on objects that are mobile, and because the application of this approach on traffic junctions did not yield desired results, a more specific approach of classification using a combination of Histogram of Oriented Gradients (HOG) and Support Vector Machines (Linear SVM) has shown to be more appropriate as the original source data can be used for training where the main benefit is the preservation of local second-order interactions, providing tolerance to local geometric misalignment and ability to work with small data samples. The features of the objects within a frame are extracted first by standardizing the feature variables and then computing the first order gradients of the frame. In the next stage, an encoding that remains robust to small changes while being sensitive to local frame content is produced. Finally, the HOG descriptors are generated and normalized again. In this way the channel histogram and spatial vector becomes the feature vector for the Linear SVM classifier. With the following parameters and setup system accuracy was around 85 to 95%. In the next phase, after cleaning protocols on collected data parameters, data will be used to research asphalt deformation effects.

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“…Choudhury et al [10] proposed a system based on Dynamic Bayesian Network (DBN). It was tested on three videos after extracting visual features such as the number of moving vehicles [11]. Other examples of vehicle detector are in [12] and in [13] where features to characterize the traffic density are extracted from video footage.…”

Section: Related Studiesmentioning

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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Vehicle detection and counting using haar feature-based classifier

Cited by 51 publications

References 9 publications

Image Clustering Method based on Particle Swarm Optimization

Image Clustering Method based on Particle Swarm Optimization

Video Data Extraction and Processing for Investigation of Vehicles’ Impact on the Asphalt Deformation Through the Prism of Computational Algorithms

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

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