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Vidya Patil

Bio: Vidya Patil is an academic researcher from Maharashtra Institute of Technology. The author has contributed to research in topics: Mass gathering. The author has an hindex of 1, co-authored 2 publications receiving 3 citations.

Papers
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Proceedings ArticleDOI
01 Sep 2016
TL;DR: Using the video monitoring device footage along with optical flow and standard deviation model to detect the crowd congestion and providing warning for managing the crowd is achieved.
Abstract: Now a day, the mass events like political rallies, musical concerts, sports matches, school gathering and pilgrimage become an issue of the public security. So, Management of crowd at public places becomes a big issue. Crowd congestion prevents the smooth flow of the crowd and causes the unsafe condition; thereby causing many people lost their life. So, it is important to overcome such situation to provide the safety on time. It can be achieved by using the video monitoring device footage along with optical flow and standard deviation model to detect the crowd congestion and providing warning for managing the crowd. Higher the congestion level higher is the risk of life and occurrences of uncertainty. Based on the level of congestion and movement it is possible to predict the future flow of crowd in that direction.

3 citations

Proceedings ArticleDOI
12 Aug 2016
TL;DR: The system detects the congestion with the help of optical flow and the standard deviation and gives warning when the congestion is detected and will help to overcome such situation.
Abstract: Now a day, the mass events like political rallies, musical concerts, sports matches, school gathering and pilgrimage become an issue of the public security. In the past the research is based on the detection of the human behaviour and for finding the hazardous location. Crowd is a collection or gathering of many people in the same area at the same time. In such situation the density of people is high and this may causes the disaster. The proposed system will help to overcome such situation. The system detects the congestion with the help of optical flow and the standard deviation and gives warning when the congestion is detected.

Cited by
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Book ChapterDOI
01 Jan 2020
TL;DR: This chapter proposes a model for activity identification of the autistic child through video recordings and examines the various activities, time delay, and factors influencing the motion of the autism child under constrained scenarios proving maximum accuracy and performance.
Abstract: Autism spectrum disorder (ASD) is a very high-flying area of research in the current era owing to its limited and on-going exploration. This chapter aims to bridge the gap of such late realization of autistic feature through machine intervention commonly known as computer vision. In this chapter, basic summarization of important characteristic features of autism and how those features could be measured and altered before a human could recognize are proposed. The chapter proposes a model for activity identification of the autistic child through video recordings. The approach is modelled in a way that consists of two phases: 1) Optical flow method detects the unusual frames based on motion pattern. 2) Each of these detected frames are fed to convolution neural network, which is trained to extract features and exactly classify if the particular frame under consideration belongs to usual or unusual class. This examines the various activities, time delay, and factors influencing the motion of the autistic child under constrained scenarios proving maximum accuracy and performance.

3 citations

Proceedings ArticleDOI
09 May 2018
TL;DR: A novel framework for accurate localization and tracking of AUAV enabled by cooperating SUAVs is proposed, with a localization algorithm called cooperation coordinate separation interactive multiple model extended Kalman filter (CoCS-IMMEKF) that simplifies the set of multiple models and eliminates the model competition of each motion direction by coordinate separation.
Abstract: Unmanned aerial vehicles (UAVs), commonly known as drones, have the potential to enable a wide variety of beneficial applications in areas such as monitoring and inspection of physical infrastructure, smart emergency/disaster response, agriculture support, and observation and study of weather phenomena including severe storms, among others. However, the increasing deployment of amateur UAVs (AUAVs) places the public safety at risk. A promising solution is to deploy surveillance UAVs (SUAVs) for the detection, localization, tracking, jamming and hunting of AUAVs. Accurate localization and tracking of AUAV is the key to the success of AUAV surveillance. In this article, we propose a novel framework for accurate localization and tracking of AUAV enabled by cooperating SUAVs. At the heart of the framework is a localization algorithm called cooperation coordinate separation interactive multiple model extended Kalman filter (CoCS-IMMEKF). This algorithm simplifies the set of multiple models and eliminates the model competition of each motion direction by coordinate separation. At the same time, this algorithm leverages the advantages of fusing multi-SUAV cooperative detection to improve the algorithm accuracy. Compared with the classical interacting multiple model unscented Kalman filter (IMMUKF) algorithm, this algorithm achieves better target estimation accuracy and higher computational efficiency, and enables good adaptability in SUAV system target localization and tracking.

3 citations

16 Aug 2018
TL;DR: Pengukuran parameter fisik pada umumnya menggunakan sistem akuisisi data dengan luaran berupa rekaman and hasil analisis data.
Abstract: Pengukuran parameter fisik pada umumnya menggunakan sistem akuisisi data dengan luaran berupa rekaman dan hasil analisis data. Analisis yang dilakukan antara lain berupa penghitungan nilai rerata dan simpangan baku. Kendati pun demikian, cara konvensional untuk melakukan penghitungan keduanya tidak dapat diterapkan untuk nilai-nilai yang dikirimkan secara kontinu selama jangka waktu tertentu. Tujuan dari penelitian ini adalah menerapkan metode Simple Moving Average dalam penghitungan rerata dan simpangan baku pada aplikasi pencatat data ukur sensor. Sistem akuisisi data digunakan untuk mengukur nilai suhu dan kelembaban yang tersusun dari sensor DHT22, pushbutton, LED, Arduino Uno R3 penampil LCD, modul komunikasi serial dan piranti komputasi. Aplikasi akuisisi data direalisasikan dengan perangkat pemrograman Processing dan telah dapat menghasilkan rekaman data berformat CSV yang menampung 8 buah kolom nilai (termasuk rerata dan simpangan baku). Rerata suhu dan kelembaban terhadap waktu, masing-masing menunjukkan nilai stabil saat t > 60s dan t > 90s. Setelah dibandingkan dengan metode konvensional untuk menghitung simpangan baku, metode Simple Moving Average menghasilkan nilai yang serupa, baik untuk suhu maupun kelembaban.