Motion detection using optical flow and standard deviation
01 Sep 2016-pp 295-300
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.
Citations
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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
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
Additional excerpts
...ΦCV = 1 T 0 0 0 1 0 0 0 0 0 0 0 0 0 0 , GCV = 1 2T 2 T 0 0 (17)...
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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.
Cites background from "Motion detection using optical flow..."
...Pada sebuah penelitian, simpangan baku ini telah dimanfaatkan untuk menentukan kepadatan manusia di dalam suatu area [9]....
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References
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30 Aug 2011
TL;DR: An automatic system for the detection and early warning of dangerous situations during mass events based on optical flow computations and detects patterns of crowd motion that are characteristic for hazardous congestions is presented.
Abstract: Tragically, mass gatherings such as music festivals, sports events or pilgrimage quite often end in terrible crowd disasters with many victims. In the past, research focused on developing physical models that model human behavior in order to simulate pedestrian flows and to identify potentially hazardous locations. However, no automatic systems for detection of dangerous motion behavior in crowds exist. In this paper, we present an automatic system for the detection and early warning of dangerous situations during mass events. It is based on optical flow computations and detects patterns of crowd motion that are characteristic for hazardous congestions. By applying an online change-point detection algorithm, the system is capable of identifying changes in pedestrian flow and thus alarms security personnel to take necessary actions.
30 citations
"Motion detection using optical flow..." refers methods in this paper
...Barbara Krousz, Christian Bauckhage [9] used the dense optical flow proposed by Farneback, but the dense optical flow is slow as compare to the sparse technique....
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23 Aug 2010
TL;DR: A system to detect and track crowds in an image sequence captured by a camera by means of pyramidal Lucas-Kanade feature tracking and a density based clustering is used to group similar vectors.
Abstract: In this paper, we present a system to detect and track crowds in an image sequence captured by a camera. In the first step, we compute optical flows by means of pyramidal Lucas-Kanade feature tracking. Afterwards, a density based clustering is used to group similar vectors. In the last step, a crowd tracker is applied to each frame, allowing us to detect and track the crowds. The output of the system is given as a graphic overlay, i.e. arrows and circles with different colors are added to the original images to visualize crowds and their movements. Evaluation results show that the system is capable of detecting certain events in the crowds, such as merging, splitting and collision.
28 citations
"Motion detection using optical flow..." refers methods in this paper
...[7] To calculate the optical flow Horn & Schuncks Method and Lucas Kanade Method [8] are used....
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01 Jan 2013
TL;DR: A robust and efficiently computed background subtraction algorithm that is capable enough to manage with local illumination changes as well as global illumination changes and to give readers a main idea of the architecture of a human motion detection system in applications.
Abstract: This paper presents a novel algorithm for motion detection from a stationary background scene to detect moving object based on background subtraction. The Motion tracking Surveillance has gained a lot of interests over the past few years. We developed a robust and efficiently computed background subtraction algorithm that is capable enough to manage with local illumination changes as well as global illumination changes. The main algorithm being discussed here are those implementing image subtraction methods and background segmentation approach. The report also is aimed to give readers a main idea of the architecture of a human motion detection system in applications. The experiment results show that the proposed method runs rapidly, robustly, exactly and accurate for the concurrent detection.
6 citations
"Motion detection using optical flow..." refers background or methods in this paper
...Optical flow is the better technique for the moving object detection [1]....
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...It detects the foreground object from the static background as the difference between the current frames [1, 2]....
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Proceedings Article•
10 Aug 2014
5 citations
14 Jul 2013
TL;DR: This study proposes a method that uses video-monitoring devices to closely monitor crowd conditions and creates a grid model to efficiently detect crowd congestion and to facilitate the analysis necessary for crowd management.
Abstract: The management and control of crowds is crucial to the maintenance of public safety. Since crowd congestion prevents the smooth flow of traffic, possibly creating crammed and potentially unsafe conditions, it is important to closely monitor crowd-congestion conditions, to provide timely data analysis and to evaluate the potential for the development of unsafe conditions. This study proposes a method that uses video-monitoring devices to closely monitor crowd conditions and creates a grid model to efficiently detect crowd congestion and to facilitate the analysis necessary for crowd management.
2 citations