In this article

Introduction

Concept

Geo-Spatial Data Extraction

Geo-Spatial Data Mapping

Geo-Spatial Data Analysis

Roles and Permissions

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Introduction

In today’s digitized world, one of the recent and finest inventions is drones. Due to their ability to reach out to remote areas, follow a targeted location while intelligently avoiding hindrances and collect meaningful data along its route, they are heavily used in the domains of surveillance and policing acting as alarms for suspicious activity as well as a primary data-capturing device for agricultural lands. Drones have cameras installed within them to record live footage of the path they are traversing along with advanced sensing equipment for registering time-series data from its surroundings which includes temperature, height, atmospheric pressure. These data points are then mapped onto the latitude/longitude of the location pin where the drone was present at that point in time.


To watch video recorded with drone while analyzing the data collected through the sensor is not easy because it requires KLV (Key Length Value) data extraction and mapping technique. Most of the Digital Content Management systems are unable to meet this requirement yet but VIDIZMO again takes lead in Digital Content Management system providers by providing this functionality.


Concept

Key Length Value (KLV) is a highly efficient standard for data encoding which is often used to embed information in videos. It is an SMPTE (The Society of Motion Picture and Television Engineers) standard adopted by the MISB (Motion Imagery standard Board) for digital encoding of metadata in motion imagery streams. In its most fundamental form, the KLV data element is the combination of three units:


  • Key: Identifies the data recorded from drone’s sensors
  • Length: The length specifies the data length
  • Value: Value is the data


KLV data elements are packaged in KLV Protocol Data Units (PDU). Each PDU organizes KLV data elements into a complete body of information. PDUs are usually tagged with a timestamp asserting that this is the data as it existed at a specific date and time. Timestamps also allow correlation of KLV PDUs with other data, such as video and audio.


Please read further to understand how VIDIZMO extracts, maps and help you analyze videos recorded with drones.


Geo-Spatial Data Extraction 

Since most data and measurements gathered by drones can be associated with locations and, therefore, needed to be placed on the map which is called geo-spatial data. For extracting that video metadata (data associated with video recorded from drone), VIDIZMO uses its on-premise local encoding library to extract KLV information from MPEG TS, and then parses it to map information against a series of timed data. In this way, one can watch video while analyzing the data captured at that point in time simultaneously. 


Many a times, a video is recorded using drone which is surveilling a target having various sensors in it to record crucial information from the surrounding other than the camera footageThese data files are then sometimes not embedded within the video file, and are obtained as a separate file. Therefore, VIDIZMO also provisions the upload of external Timed Data (srtvttgpx) metadata files separately into a video. So that, one can analyze data captured from multiple sensors on a single map.


Click to, see: Click to, see: How to Upload Timed Data in VIDIZMO 


Geo-Spatial Data Mapping 

At the time of video playback, VIDIZMO extracts the KLV data from that video or read the Timed Data file uploaded with the drone video and plot it on the maps. So that every bit of the data collected from the drone sensor could be synced with video, and you can analyze Geo-Spatial data while watching the video seamlessly. 


Geo-Spatial Data Analysis 

Using spatial data, one knows both what is present and where it is. Therefore, VIDZMO provides an interface similar to the Geographical Information System (GIS), where data (collected from the drone video which is processed at each millisecond) is managed as layers and can graphically be combined using analytical operators, in case of VIDIZMO these analytical operators are Sensor Location (trajectory of the drone recording the video), Target Location (trajectory of the target device) and Frame Center Location (relative central position of the frame currently being recorded).  


VIDIZMO uses Google Maps for plotting the geo-spatial data, positioning of tracking devices and target, and KLV data collected from drones.


Click to, see: How to Analyze Geospatial Data in Video Recorded Using Drone. In this article you will be able to find the information about how to; 


  • Track drone location and target (if any) location 
  • Turn on/off one or more additional data layers against the recorded footage such as temperature, humidity, differential pressure, etc
  • View changes in KLV data associated with a timestamp as the video seeks forward 
  • Can switch to full-screen map mode 
  • Download a report of all KLV data within a video or KLV data against a particular timestamp in a video


Roles and Permissions 

In this section, we will discuss various actions related to the KLV Data extraction and Mapping feature for Geo-Spatial Data Analysis and roles that are allowed to handle/use this feature. 


RolesPermissions
Moderator+
  • Upload videos with KLV Data 
  • Upload metadata files (vtt, gpx, srt) to sync drone KLV information with video. 
  • May toggle geotag view on/off to control whether or not to display KLV data along with player
  • Perform geospatial data analysis
Contributors
  • Upload videos with KLV Data 
  • Can watch and analyze videos uploaded by them
Viewers
  • May perform geospatial data analysis if they are granted access to a drone video where insights have been enabled
Anonymous
  • May perform geo-spatial data analysis if the portal is set to be accessed anonymously  



Click to, see: Understanding User Roles for a complete understanding of the roles discussed above.