Search results for: V. Sushant

Authors: Vicky Suri, Monika Suri Grover, Raghav Gupta, Shipra Asija, Sulabh Arya, Sushant Jain

Abstract:

With today's fast lifestyles and busy schedule, nuclear families are becoming popular. Thus, the elderly members of these families are often neglected. This has lead to the popularity of the concept of Community living for the aged. The elders reside at a centre, which is controlled by the MANAGER. The manager takes responsibility of the functioning of the centre which includes taking care of 'residents' at the centre along with managing the daily chores of the centre, which he accomplishes with the help of a number of staff members and volunteers Often the Manager is not an employee but a volunteer. In such cases especially, time is an important constraint. A system, which provides an easy and efficient manner of managing the working of an old age home in detail, will prove to be of great benefit. We have developed a P.C. based organizer used to monitor the various activities of an old age home. It is an effective and easy-to-use system which will enable the manager to keep an account of all the residents, their accounts, staff members, volunteers, the centre-s logistic requirements etc. It is thus, a comprehensive 'Organizer' for Old Age Homes.

Keywords: Old Age Home Organizer, HelpAge India.

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Authors: H. Baskar, A. S. Chakravarthy, P. Garg, D. Goel, A. S. Raj, K. Kumar, Lakshya, R. Parvatham, V. Sushant, B. Kumar Rout

Abstract:

In this paper, we present a computer vision task called nighttime dehaze-enhancement. This task aims to jointly perform dehazing and lightness enhancement. Our task fundamentally differs from nighttime dehazing – our goal is to jointly dehaze and enhance scenes, while nighttime dehazing aims to dehaze scenes under a nighttime setting. In order to facilitate further research on this task, we release a benchmark dataset called Reside-β Night dataset, consisting of 4122 nighttime hazed images from 2061 scenes and 2061 ground truth images. Moreover, we also propose a network called NDENet (Nighttime Dehaze-Enhancement Network), which jointly performs dehazing and low-light enhancement in an end-to-end manner. We evaluate our method on the proposed benchmark and achieve Structural Index Similarity (SSIM) of 0.8962 and Peak Signal to Noise Ratio (PSNR) of 26.25. We also compare our network with other baseline networks on our benchmark to demonstrate the effectiveness of our approach. We believe that nighttime dehaze-enhancement is an essential task particularly for autonomous navigation applications, and hope that our work will open up new frontiers in research. The code for our network is made publicly available.

Keywords: Dehazing, image enhancement, nighttime, computer vision.

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