Video Data Sources and Applications

AbstractEducational research into instructional quality would benefit from macro- and meso-level instructional data – such as achievement data or large-scale student surveys – in relation to data from the micro level – such as detailed analyses of classroom practices. Several scholars have specifically asked for studies that correlate achievement data with records of learning processes and teaching strategies, and ongoing projects attempting to do so have shown promising results. Linking different data sources on instructional quality is quite demanding because it requires a concerted effort by researchers from different fields of expertise and different traditions. A main ambition of our ongoing research project is precisely to advance such integration. As the title of the project reveals, we are dedicated to Linking Instruction and Student Achievement (LISA). In this article, we start by providing a theoretical background and status of knowledge related to instructional quality. We go on to argue that video data has shown particular promise in studies aiming to obtain systematic data from a range of classrooms in order to compare classroom practices. We then present the three components of the LISA project’s design – student perception surveys, systematic classroom observation, and achievement gains in national tests – and the value of combining these three data sources. Finally, we will outline some of our findings thus far and point to future research possibilities.Key words: instructional quality; classroom practices; video studies; mathematics; language arts Å koble undervisning med elevprestasjoner - Forskningsdesign for en ny generasjon klasseromsstudierSammendragFor å studere undervisningskvalitet vil det være en fordel å kombinere data fra et makro og meso- nivå med detaljerte studier av hva som skjer i klasserommet. Flere har etterlyst studier som ser på sammenhenger mellom målbar faglig fremgang og lærerens undervisning. Å få til slike studier er krevende, da det forutsetter et tett samarbeid mellom forskere fra ulike felt med ulik ekspertise innenfor nokså ulike forskningstradisjoner. En hovedambisjon i vårt pågående forskningsprosjekt er nettopp å få til en slik integrasjon. Som tittelen avslører, er vi dedikert til «Linking Instruction and Student Achievement (LISA)». I denne artikkelen presenterer vi det teoretiske og empiriske grunnlaget knyttet til undervisningskvalitet. Videre argumenterer vi for verdien av videodata i studier som sammenligner undervisningspraksiser fra ulike klasserom på en systematisk måte. Deretter presenterer vi de tre datakildene i LISA-prosjektets forskningsdesign – spørreskjemaer til elever om deres oppfatninger om lærerens undervisning, systematiske klasseromsobservasjoner, og målt fremgang på nasjonale prøver i lesing og regning. Verdien av å kombinere nettopp disse tre datakildene vil også bli diskutert. Avslutningsvis deler vi noen av våre tidlige forskningsfunn.Nøkkelord: undervisningskvalitet; klasseromspraksis; video studier; matematikk; norskfaget

Download Full-text

Information Acquisition from Audio-Video-Data Sources: An Experimental Study on Remote Diagnosis

Telemedicine Journal ◽

10.1089/107830299312104 ◽

1999 ◽

Vol 5 (2) ◽

pp. 139-155 ◽

Cited By ~ 6

Author(s):

Yan Xiao ◽

Colin MacKenzie ◽

Judith Orasanu ◽

Richard Spencer ◽

Amaly Rahman ◽

...

Keyword(s):

Experimental Study ◽

Information Acquisition ◽

Video Data ◽

Data Sources ◽

Remote Diagnosis ◽

Audio Video

Download Full-text

Capturing Christmas: The Sensory Potential of Data from Participant Produced Video

Sociological Research Online ◽

10.5153/sro.2580 ◽

2012 ◽

Vol 17 (1) ◽

pp. 47-65 ◽

Cited By ~ 20

Author(s):

Stewart Muir ◽

Jennifer Mason

Keyword(s):

Family Life ◽

Ethical Issues ◽

Video Data ◽

Interview Data ◽

Data Sources ◽

Sociological Analysis ◽

Personal Lives ◽

Family Histories

In this paper, we discuss our use of participant-produced digital footage of family Christmases, collected as part of a larger project exploring family backgrounds and family traditions. The audio-visual recording (and subsequent dissemination) of these otherwise difficult-to-access domestic celebrations provides important insights into the multi-dimensional, multisensory, physical and situational nature of such family traditions. With their blend of genre styles - from narrated documentary to home-movie style wobbly camera work - the ‘Christmas videos’ show both conscious ‘displays’ of family life and practice (performed for the camera, for the participants and for posterity) and largely unscripted, and sometimes noisily chaotic, interactions. Although videos cannot provide unmediated access into what such traditions are ‘really like’, in combination with our other data sources the footage has helped to push our thinking about family traditions as being at once intellectualised productions and a series of bodily engagements with a host of practices, understandings, knowledges, family histories, things and people. This form of ‘backstage’ analytical usage of the video data has been very productive for us. However, we argue that there are ethical issues in publicly presenting such data alongside other forms of data, eg interview data, in a deep sociological analysis of people's personal lives. There is the potential not only for the production of incisive knowledge and insight, but also for a prying and distinctively sociological intrusiveness, and sociologists need to think carefully about how to proceed.

Download Full-text

Identifying High-Risk Intersections for Walking and Bicycling Using Multiple Data Sources in the City of San Diego

Journal of Advanced Transportation ◽

10.1155/2019/9072358 ◽

2019 ◽

Vol 2019 ◽

pp. 1-15 ◽

Cited By ~ 3

Author(s):

Mahdie Hasani ◽

Arash Jahangiri ◽

Ipek Nese Sener ◽

Sirajum Munira ◽

Justin M. Owens ◽

...

Keyword(s):

High Risk ◽

Video Processing ◽

San Diego ◽

Injury Severity ◽

Spatial Scales ◽

Video Data ◽

Data Sources ◽

Multiple Data Sources ◽

Multiple Data ◽

The City

Over the last decade, demand for active transportation modes such as walking and bicycling has increased. While it is desirable to provide high levels of safety for these eco-friendly modes of travel, unfortunately, the overall percentage of pedestrian and bicycle fatalities increased from 13% to 18% of total road-related fatalities in the last decade. In San Diego County, although the total number of pedestrian and bicyclist fatalities decreased over the same period of time, a similar trend with a more drastic change is observed; the overall percentage of pedestrian and bicycle fatalities increased from 19.5% to 31.8%. This study aims to estimate pedestrian and bicyclist exposure and identify signalized intersections with highest risk for walking and bicycling within the city of San Diego, California, USA. Multiple data sources such as automated pedestrian and bicycle counters, video cameras, and crash data were utilized. Data mining techniques, a new sampling strategy, and automated video processing methods were adopted to demonstrate a holistic approach that can be applied to identify facilities with highest need of improvement. Cluster analysis coupled with stratification was employed to select a representative sample of intersections for data collection. Automated pedestrian and bicycle counting models utilized in this study reached a high accuracy, provided certain conditions exist in video data. Results from exposure modeling showed that pedestrian and bicyclist volume was characterized by transportation network, population, traffic generators, and land use variables. There were both similarities and differences between pedestrian and bicycle models, including different spatial scales of influence by mode. Additionally, the study quantified risk incorporating injury severity levels, frequency of victims, distance crossed, and exposure into a single equation. It was found that not all intersections with the highest number of pedestrian and bicyclist victims were identified as high-risk after exposure and other factors such as crash severity were taken into account.

Download Full-text

OBJECT COUNTING PADA DATA VIDEO

KLIK - KUMPULAN JURNAL ILMU KOMPUTER ◽

10.20527/klik.v7i1.307 ◽

2020 ◽

Vol 7 (1) ◽

pp. 92

Author(s):

Rudy Herteno ◽

M. Reza Faisal ◽

Radityo A Nugroho ◽

Friska Abadi ◽

Rahmat Ramadhani

Keyword(s):

Image Processing ◽

Video Processing ◽

Learning Algorithm ◽

Video Data ◽

Data Sources ◽

Small Data ◽

Image Processing Algorithm ◽

Computing Device ◽

Server Side ◽

Object Counting

One object counting implementation is counting the number of road users from video data sources obtained from CCTV streaming. Video processing on CCTV is usually done on the server side by sending video data. If the need is only to determine the density of traffic, then the method is considered too expensive to be implemented because of the cost of internet connection and bandwidth that must be spent. The solution is to use a small computing device that can process the video first, and the calculation results are sent to the server regularly. In this study, a comparison between the Tensorflow Object Counting learning algorithm and the MOG2 Background Subtractor image processing algorithm with the aim to determine the accuracy of the calculation. The result is known that better accuracy is given by the MOG2 Background Subtractor technique and also the process is carried out using only a small percentage of the amount of memory and processor compared to the Tensorflow Object Counting technique. MOG2 Background Substractor technique is expected to be used on devices that have small data sourcesKeywords : Object Counting, Tensorflow, MOG2 Background SubstractorSalah satu implementasi object counting adalah menghitung jumlah pengguna jalan dari sumber data video yang didapat dari streaming CCTV. Pemprosesan video pada CCTV biasanya dilakukan disisi server dengan mengirimkan data video. Jika keperluannya hanya untuk mengetahui kepadatan lalu lintas, maka cara tersebut dinilai terlalu mahal untuk diimplementasikan karena biaya koneksi internet dan bandwidth yang harus dikeluarkan. Pemecahannya adalah menggunakan perangkat komputasi kecil yang dapat memproses video tersebut terlebih dahulu, dan hasil perhitungannya dikirimkan ke server secara berkala. Pada penelitian ini dilakukan perbandingan antara algoritma pembelajaran Tensorflow Object Counting dan algoritma image processing MOG2 Background Substractor dengan tujuan untuk mengetahui akurasi penghitungan. Hasilnya diketahui akurasi yang lebih baik diberikan oleh teknik MOG2 Background Substractor dan juga proses yang dilakukan hanya menggunakan prosentase jumlah memori dan prosessor yang kecil dibandingkan teknik Tensorflow Object Counting. Sehingga teknik MOG2 Background Substractor ini diharapkan dapat digunakan pada perangkat yang memiliki sumber data kecil. Kata kunci : Object Counting, Tensorflow, MOG2 Background Substractor.

Download Full-text