Object detection and identification using SURF and BoW model

Farooq, Javeria

doi:10.1109/icecube.2016.7495245

Cited by 27 publications

(10 citation statements)

References 10 publications

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“…Basically, object detection can be categorised into three aspects: appearance-based, colour-based and feature-based. All of these methods have their advantages and limitations [ 38 ]. Here we have decided to use the feature-based technique because it finds the interest points of an object in image and matches them to the object in another image of similar scene.…”

Section: Related Workmentioning

confidence: 99%

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Pose Estimation of a Mobile Robot Based on Fusion of IMU Data and Vision Data Using an Extended Kalman Filter

Alatise

Hancke

2017

Sensors

166

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Using a single sensor to determine the pose estimation of a device cannot give accurate results. This paper presents a fusion of an inertial sensor of six degrees of freedom (6-DoF) which comprises the 3-axis of an accelerometer and the 3-axis of a gyroscope, and a vision to determine a low-cost and accurate position for an autonomous mobile robot. For vision, a monocular vision-based object detection algorithm speeded-up robust feature (SURF) and random sample consensus (RANSAC) algorithms were integrated and used to recognize a sample object in several images taken. As against the conventional method that depend on point-tracking, RANSAC uses an iterative method to estimate the parameters of a mathematical model from a set of captured data which contains outliers. With SURF and RANSAC, improved accuracy is certain; this is because of their ability to find interest points (features) under different viewing conditions using a Hessain matrix. This approach is proposed because of its simple implementation, low cost, and improved accuracy. With an extended Kalman filter (EKF), data from inertial sensors and a camera were fused to estimate the position and orientation of the mobile robot. All these sensors were mounted on the mobile robot to obtain an accurate localization. An indoor experiment was carried out to validate and evaluate the performance. Experimental results show that the proposed method is fast in computation, reliable and robust, and can be considered for practical applications. The performance of the experiments was verified by the ground truth data and root mean square errors (RMSEs).

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Section: Related Workmentioning

confidence: 99%

“…SURF is used to detect key points and to generate its descriptors. Its feature vector is based on the Haar Wavelet response around the interested features [ 38 ]. SURF is scale-and rotation-invariant, which means that, even with variations of the size and rotation of an image, SURF can find key points.…”

Section: Related Workmentioning

confidence: 99%

Pose Estimation of a Mobile Robot Based on Fusion of IMU Data and Vision Data Using an Extended Kalman Filter

Alatise

Hancke

2017

Sensors

166

View full text Add to dashboard Cite

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“…Basically, object detection can be categorised into three aspects: appearance based, color based and features based. All these methods have their advantages and limitations [80].…”

Section: Object Recognition and Feature Matchingmentioning

confidence: 99%

“…SURF is used to detect key points and to generate its descriptors. Its feature vector is based on the Haar Wavelet response around the interested features [80]. SURF is a scale-and rotation-invariant, that means, even with variations on the size and on the rotation of an image, SURF can find key points.…”

Section: A Speeded-up Robust Features (Surf)mentioning

confidence: 99%

A Review on Challenges of Autonomous Mobile Robot and Sensor Fusion Methods

2020

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Autonomous mobile robots are becoming more prominent in recent time because of their relevance and applications to the world today. Their ability to navigate in an environment without a need for physical or electro-mechanical guidance devices has made it more promising and useful. The use of autonomous mobile robots is emerging in different sectors such as companies, industries, hospital, institutions, agriculture and homes to improve services and daily activities. Due to technology advancement, the demand for mobile robot has increased due to the task they perform and services they render such as carrying heavy objects, monitoring, search and rescue missions, etc. Various studies have been carried out by researchers on the importance of mobile robot, its applications and challenges. This survey paper unravels the current literatures, the challenges mobile robot is being faced with. A comprehensive study on devices/sensors and prevalent sensor fusion techniques developed for tackling issues like localization, estimation and navigation in mobile robot are presented as well in which they are organised according to relevance, strengths and weaknesses. The study therefore gives good direction for further investigation on developing methods to deal with the discrepancies faced with autonomous mobile robot.

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“…The results describe that even though SIFT is more robust, SURF is much faster and has good accuracy rate. Object detection and recognition methods using SURF and BoW are described in the article [5]. According to its results, while using BoW method, SURF feature extractor and SVM classifier give best results for recognition.…”

Section: Existing Workmentioning

confidence: 99%

An analysis of Feature extraction and Classification Algorithms for Dangerous Object Detection

Kibria

Hasan

2017

2017 2nd International Conference on Electrical &Amp; Electronic Engineering (ICEEE)

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Abstract-One of the important practical applications of object detection and image classification can be for security enhancement. If dangerous objects e.g. knives can be identified automatically, then a lot of violence can be prevented. For this purpose, various different algorithms and methods are out there that can be used. In this paper, four of them have been investigated to find out which can identify knives from a dataset of images more accurately. Among Bag of Words, HOG-SVM, CNN and pre-trained Alexnet CNN, the deep learning CNN methods are found to give best results, though they consume significantly more resources.

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Object detection and identification using SURF and BoW model

Cited by 27 publications

References 10 publications

Pose Estimation of a Mobile Robot Based on Fusion of IMU Data and Vision Data Using an Extended Kalman Filter

Pose Estimation of a Mobile Robot Based on Fusion of IMU Data and Vision Data Using an Extended Kalman Filter

A Review on Challenges of Autonomous Mobile Robot and Sensor Fusion Methods

An analysis of Feature extraction and Classification Algorithms for Dangerous Object Detection

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