Object detection and classification by cascade object training

Chowdhury, Ahmed Masud; Jabin, Jafrina; Efaz, Erteza Tawsif; Adnan, Ehtesham; Habib, Ashfia Binte

doi:10.1109/iemtronics51293.2020.9216377

Cited by 5 publications

(4 citation statements)

References 16 publications

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“…Chowdhury et al [20] present a cascaded object detection and classification methodology. The model's training, encompassing 50 positive images, employs Cascade Trainer Graphical User Interface (GUI), while MATLAB facilitates testing.…”

Section: Background Of the Studymentioning

confidence: 99%

Precision Face Mask Detection in Crowded Environment using Machine Vision

Alsayaydeh,

Yusof,

Lin

et al. 2024

IJACSA

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In the face of rampant global disease transmission, effective preventive strategies are imperative. This study tackles the challenge of ensuring compliance in crowded settings by developing a sophisticated face mask detection system. Utilizing MATLAB and the Cascade Object detector, the system focuses on detecting white surgical masks in frontal images. Training the system is critical for accuracy; therefore, cross-validation is employed due to limited data. The results reveal accuracies of 76.67% for initial training, 67.50% for a 9:11 cropping ratio, and 89.17% for a 9:4:7 cropping ratio, highlighting the system's remarkable precision in mask detection. Looking ahead, the system's adaptability can be further expanded to include various mask colors and types, extending its effectiveness beyond COVID-19 to combat a range of respiratory illnesses. This research represents a significant advancement in reinforcing preventive measures against future disease outbreaks, especially in densely populated environments, contributing significantly to global public health and safety initiatives.

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Section: Background Of the Studymentioning

confidence: 99%

Precision Face Mask Detection in Crowded Environment using Machine Vision

Alsayaydeh,

Yusof,

Lin

et al. 2024

IJACSA

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“…The weights are changed for the second iteration (the new selected feature), and the samples that the first iteration incorrectly categorised receive greater weights [7,22,23]. AdaBoost algorithm is used along with cascade classifier to combine the weak features that extracted from each level in order to obtain strong feature used to classify ball in input images [19].…”

Section: Target Detectionmentioning

confidence: 99%

“…However, in scenarios with busy backgrounds, a false ball detection occurred. Chowdhury et al [19] suggested an algorithm to detect cups in images based on Viola Jones algorithm. During the training stage, they applied their algorithm to a collection of positive and negative datasets that they downloaded from the internet.…”

Section: Introductionmentioning

confidence: 99%

Target Detection in Video Images Using HOG-Based Cascade Classifier

Namah¹,

Mirza²,

Al-Zuky³

2022

RIA

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Detecting small objects using computer vision is a challenging task due to their small size in the image and therefore the lack of features when describing them. In this paper, a computer was trained to detect three small balls using 20 levels of the AdaBoost cascade classifier. The features of the balls in each level are described using the HOG feature descriptor. Three balls were recorded in practice at various distances (d = 2, 3, 4, ..., 10 m) from the camera and the targets (balls). The frames are then taken from the videos and resized using five magnification factors (RS = 1, 3, 5, 7, and 9) to make the balls seem as they should. According to the results, the detection rate of balls at all distances was 80% when using the magnification factor RS = 1, 90% when using the magnification factor RS = 3, 5, and 7, and 100% when using the magnification factor RS = 9. The suggested approach was also used in calculating the height and width of the detected balls. The overall results indicated that the height and width of the balls dwindle as the distance between the camera and the targets increases.

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“…detected means then the microcontroller which is connected to it will start counting and send it to the the cloud then huge traffic is cleared out by means of the turning green light and the red light will be switched on for the least count irrespective of the time schedule in real time, here COCO dataset is used to find out the efficiency of the prediction. Chowdhury et al (2020) In this, the cup and saucers of different colors are detected out among the various objects by means of cascade classifier called HAAR-a machine learning algorithm which makes use of the number of images of the same cup and saucers in order to train a classifier and the implementation of this algorithm is carried out by OpenCV to detect the other things and labeling . The testing is carried out by MATLAB and the accuracy prediction can be carried out by using Evaluation Board (EVB).…”

Section: Objectivesmentioning

confidence: 99%

Real-time Object Prediction by Collaboration of IOT and Deep Learning

Parimala,

Bhoopal,

kumar

et al. 2022

Proceedings of the International Conference on Industrial Engineering and Operations Management

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Object detection or the prediction of the objects in the image form can be performed in tremendous ways as a general means. Here we are detecting the objects by the collaboration of the deep-learning along with the IOT technology, so that the predicting system or PC will be in one place and the capturing smart phones will be in someother place, using IOT the auto capturing, and the detection of object along will label of name is achieved with one of the pre-trained neural networks in deep learning called Google net. So as to reduce the training time and the accurate prediction of the object can be obtained. The object detection is probability checked out or compared out between the prediction accuracy inside state and the other state. And the time taken for the prediction of the objects is also made into concern. In this paper the simple smart phones which can be available with everyone's hand the object detection is handled out in the real-time and predicted with the personal computer with the booming technologies.

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Object detection and classification by cascade object training

Cited by 5 publications

References 16 publications

Precision Face Mask Detection in Crowded Environment using Machine Vision

Precision Face Mask Detection in Crowded Environment using Machine Vision

Target Detection in Video Images Using HOG-Based Cascade Classifier

Real-time Object Prediction by Collaboration of IOT and Deep Learning

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