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dc.contributor.authorHARIR, MOhammed EL-Amine-
dc.contributor.authorHARRIR, HAbib Abdelghani-
dc.descriptionSupervisor : Mr Mohamed ELARBI BOUDIHIR Mr Mohammed Yassine KAZI TANIen_US
dc.description.abstractThe ubiquitous and wide applications like scene understanding, video surveillance, robotics, and self-driving systems triggered vast research in the domain of computer vision in the most recent decade. Being the core of all these applications, visual recognition systems which encompasses image classification, localization and detection have achieved great research momentum[1]. Due to significant development in neural networks especially deep learning, these visual recognition systems have attained remarkable performance. Object detection is one of these domains witnessing great success in computer vision. Detecting objects in real-time and converting them into an audio output was a challenging task. Recent advancement in computer vision had allowed the development of various real-time objected detection applications. This paper describes a simple android app that would helped the visually impaired people in understanding their surroundings. The information about the surrounding environment was captured through a phone camera where real-time objected recognition through tensorflow’s objected detection api was done. The detected objects were then converted into an audio output used android’s texted to speech library. Tensorflow lite made the offline processing of complex algorithms simple[2].en_US
dc.subjectComputer Visionen_US
dc.subjectVisual Recognition,Neural Networksen_US
dc.subjectTensor Flow Liteen_US
dc.subjectMachine Learningen_US
dc.subjectDeep Learningen_US
dc.subjectObject Recognitionen_US
dc.subjectAnd Androiden_US
dc.titleReal time Object detection for visually impaireden_US
Appears in Collections:Ingénieur

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