Object recognition for blind people based on features extraction

Jabnoun, Hanen; Benzarti, Faouzi; Amiri, Hamid

doi:10.1109/ipas.2014.7043293

Cited by 17 publications

(7 citation statements)

References 13 publications

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“…The ORB algorithm's speed is the fastest of the SIFT and SURF algorithms based on the experimental results. In a previous study [5], the accuracy and speed in processing a video frame in the SIFT and SURF algorithms are inversely related. In this study, besides the ORB algorithm is the fastest in processing a frame, the L 5 value of the ORB algorithm is 0.81, which is not much different from the SIFT algorithm of 0.97 and better than the SURF algorithm, which is only 0.73.…”

Section: E Discussionmentioning

confidence: 94%

“…The primary stages in video processing are pre-processing by extracting video into frames and extracting features from the frame. Research conducted by Jabnoun et al [5] processed all video frames to detect household appliance objects. Processing all frames make delay in real-time video processing.…”

Section: Introductionmentioning

confidence: 99%

“…The next stage is feature extraction on query image and keyframe. Some feature extraction algorithm research, including objects searching on video conducted by Jabnoun et al [5] in the case of object searching for household appliances. The results showed that the SIFT algorithm results in an accuracy of 82% with a processing time of 30 seconds, whereas the SURF algorithm results in 18% accuracy with a processing time of 9 seconds for matching feature.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Object Searching on Real-Time Video Using Oriented FAST and Rotated BRIEF Algorithm

Adhinata¹,

Harjoko²,

Wahyono³

2021

International Journal on Advanced Science, Engineering and Information Technology

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The pre-processing and feature extraction stages are the primary stages in object searching on video data. Processing video in all frames is inefficient. Frames that have the same information should only be once processed to the next stage. Then, the feature extraction algorithm that is often used to process video frames is SIFT and SURF. The SIFT algorithm is very accurate but slow. On the other hand, the SURF algorithm is fast but less accurate. Therefore, the requirement for keyframe selection and feature extraction methods is fast and accurate in object searching on real-time video. Video is pre-processed by extracting video into frames. Then, the mutual information entropy method is used for keyframe selection. Keyframes are extracted using the ORB algorithm. The multiple object detection in the video is done by clustering on features. The feature extraction results on each cluster are matched with the results of the feature from the query image. Matching results from keyframe on video with the query image is used to retrieve the video's frame information. The experiment shows that keyframe selection is beneficial in real-time video data processing because the keyframe selection speed is faster than feature extraction on each frame. Then, feature extraction using the ORB algorithm results 2 times faster speed results than SIFT and SURF algorithms with values not so different from SIFT algorithm. This study's results can be developed as a security warning system in public places, especially by security in providing evidence of criminal cases from videos.

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Section: E Discussionmentioning

confidence: 94%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Object Searching on Real-Time Video Using Oriented FAST and Rotated BRIEF Algorithm

Adhinata¹,

Harjoko²,

Wahyono³

2021

International Journal on Advanced Science, Engineering and Information Technology

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“…Panchal et al presented, in [30], a new approach for recognizing text from scene images and to convert it into speech so that it can assist VI people. Jabnoun and colleagues reported in [31] an object recognition system based on SIFT (Scale Invariant Features Transform) and SURF algorithms to assist VI people during navigation. Ciobanu et al introduced, in [32], a method for detecting indoor staircases by employing IMU (inertial measurement unit) sensors and processing depth images in order to aid VI people in unfamiliar environments.…”

Section: State Of the Art On Innovative Assistive Technology Devices mentioning

confidence: 99%

Solar-Powered Deep Learning-Based Recognition System of Daily Used Objects and Human Faces for Assistance of the Visually Impaired

et al. 2020

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This paper introduces a novel low-cost solar-powered wearable assistive technology (AT) device, whose aim is to provide continuous, real-time object recognition to ease the finding of the objects for visually impaired (VI) people in daily life. The system consists of three major components: a miniature low-cost camera, a system on module (SoM) computing unit, and an ultrasonic sensor. The first is worn on the user’s eyeglasses and acquires real-time video of the nearby space. The second is worn as a belt and runs deep learning-based methods and spatial algorithms which process the video coming from the camera performing objects’ detection and recognition. The third assists on positioning the objects found in the surrounding space. The developed device provides audible descriptive sentences as feedback to the user involving the objects recognized and their position referenced to the user gaze. After a proper power consumption analysis, a wearable solar harvesting system, integrated with the developed AT device, has been designed and tested to extend the energy autonomy in the different operating modes and scenarios. Experimental results obtained with the developed low-cost AT device have demonstrated an accurate and reliable real-time object identification with an 86% correct recognition rate and 215 ms average time interval (in case of high-speed SoM operating mode) for the image processing. The proposed system is capable of recognizing the 91 objects offered by the Microsoft Common Objects in Context (COCO) dataset plus several custom objects and human faces. In addition, a simple and scalable methodology for using image datasets and training of Convolutional Neural Networks (CNNs) is introduced to add objects to the system and increase its repertory. It is also demonstrated that comprehensive trainings involving 100 images per targeted object achieve 89% recognition rates, while fast trainings with only 12 images achieve acceptable recognition rates of 55%.

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“…al. [5] in their paper said that object recognition for the visually impaired based on features extraction provided an overview of various visual alternative systems developed in the recent years. This method is based on interpretation, video analysis and feature extraction .They give the results of comparison of SIFT and SURF in which they concluded that SURF is faster than SIFT ,however SIFT is robust when the matches findings, scale variations are considered.…”

Section: Survey Detailsmentioning

confidence: 99%

Real Time Detection and Recognition of Hand Held Objects to Assist Blind People

2017

IJAERD

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There are approximately 315 million people with visual imparity worldwide. The number of these people is also increasing as this generation ages. Recent development in computer vision, digital cameras, and portable computers make it possible to assist these people by developing camera-based products that incorporate computer vision technology. We propose a camera-based object recognition application to help visually impaired people to recognize labeled and unlabelled hand held objects in their daily lives. To differentiate the object from complex backgrounds or other surrounding objects in the camera view, we first propose a motion based method to define a region of interest (ROI) in the image by asking the user to hold the object which is efficient and effective. Reference objects and test images are accompanied by local features (regions, interest points, etc.). If the test image get match with reference image then the output signal created in the form of audio. Output audio is generated and sent towards head-phone through which the blind user could hear the information about the product which is held on hand. In this proposed system we use speeded up robust features (SURF) for tasks such as object recognition, classification or 3D reconstruction, image registration.

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Object recognition for blind people based on features extraction

Cited by 17 publications

References 13 publications

Object Searching on Real-Time Video Using Oriented FAST and Rotated BRIEF Algorithm

Object Searching on Real-Time Video Using Oriented FAST and Rotated BRIEF Algorithm

Solar-Powered Deep Learning-Based Recognition System of Daily Used Objects and Human Faces for Assistance of the Visually Impaired

Real Time Detection and Recognition of Hand Held Objects to Assist Blind People

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