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Abstract
In this paper, we present a portable camera-based method for helping visually impaired (VI) people to recognize multiple objects in images. This method relies on a novel multi-label convolutional support vector machine (CSVM) network for coarse description of images. The core idea of CSVM is to use a set of linear SVMs as filter banks for feature map generation. During the training phase, the weights of the SVM filters are obtained using a forward-supervised learning strategy unlike the backpropagation algorithm used in standard convolutional neural networks (CNNs). To handle multi-label detection, we introduce a multi-branch CSVM architecture, where each branch will be used for detecting one object in the image. This architecture exploits the correlation between the objects present in the image by means of an opportune fusion mechanism of the intermediate outputs provided by the convolution layers of each branch. The high-level reasoning of the network is done through binary classification SVMs for predicting the presence/absence of objects in the image. The experiments obtained on two indoor datasets and one outdoor dataset acquired from a portable camera mounted on a lightweight shield worn by the user, and connected via a USB wire to a laptop processing unit are reported and discussed.
Highlights
Chronic blindness may occur as an eventual result of various causes, such as cataract, glaucoma, age-related macular degeneration, corneal opacities, diabetic retinopathy, trachoma, and eye conditions in children [1]
We propose an alternative solution suitable for datasets with limited training samples mainly based on convolutional SVM networks (CSVMs)
Descriptionwe evaluate the proposed method on three datasets taken by a portable camera mountedInonthe a lightweight shield worn by the user, and connected via a USB wire to a laptop processing experiments, we evaluate the proposed method on three datasets taken by a portable unit.camera
Summary
Chronic blindness may occur as an eventual result of various causes, such as cataract, glaucoma, age-related macular degeneration, corneal opacities, diabetic retinopathy, trachoma, and eye conditions in children (e.g., caused by vitamin A deficiency) [1]. Organization, as per October 2018 [1], indicate that 1.3 billion people suffer from some form of vision impairment, including 36 million people who are considered blind. These facts highlight an urgent need to improve the quality of life for people with vision disability, or at least to lessen its consequences. Towards achieving the earlier endeavor, assistive technology ought to exert an essential role On this point, the latest advances gave rise to several designs and prototypes, which can be regarded from two distinct but complementary perspectives, namely (1) assistive mobility and obstacle avoidance, and (2) object perception and recognition.
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