Retrieving Large-Scale High Density Video Target Tracksfrom Spatial Database

Chris Cummings

What’s it all about

Traffic cameras recording targets and retrieving them Cameras track targets and the data

needs to be recorded, but how are you supposed to store physical data, let alone go through it?

What is Spatial Database

Mapping and querying something of objects defined in geometric space.

Normally handle a point, line, or polygon.

3D objects can also be represented (this case)

Target tracking data

Sensors are running. Video-analytic software extract

target tracks from the video in real time.

Geometric rules are drawn on maps by using the geo-browser* to retrieve targets.

SQL query is submitted to the spatial database.

Spatial calculations are done and the tracks of targets are received.

Image from above

Frames are recorded and saved as image sequences in JPEG format with roughly 3 frames per second

Spatial inedxing

Spatial database divides space into subspace then indexes each of the subspaces.

The MS SQL server divides the space to 4 levels of grids.

These levels are divided to 3 grid densities.

Objects are then associated to cells which are touched as it traverses the grid levels.

These cells become the points for the spatial database to be indexed.

Searching for Space

Querying space is used to search area with geometric shapes that when combined make up the entire search space perfectly.

Cells that are touched by objects are compared to past cells and adjacent cells.

Spatial Grid layout

Grids can be broken down to degrees of detail. They are calculated by dividing the site size with grid

densities High = small area in meters but finer detail. Lower = larger area with less detail. Greater detail not always best.

Takes up a lot of space Takes a lot of computing time to go through the indexes 6.6 x 6 centimeters accuracy. Not tracked to centimeters so

is a waste. Configuration of a grid can be set to HHHH, LLLL, and

everything in between. Configuration is based on the situation of the

enviorment

Performance

HHHH is 8.4 times as large as LLLL due to tracking cells.

To reduce the size of the index, reduce the size of the data.

Tracklets are the points of data. For every 1,000,000 tracklets insertion time

is measured as they are put into the database.

To insert 1,000,000 tracklets it takes 1227 seconds. If the data is bigger, this can take even longer.

Insert from multiple sensors

Simulations were done using 12 and 18 sensors, putting in data.

Highest insertion capacity if inserted in batch.

Many sensors putting in 100 targets per insertion gave the best results.

Tripwire

Used like a regular trip wire would be.

The tripwire is defined as a point on all of the camera sensors.

When the point is crossed an alarm is raised and all the tracklets are found by finding all which intersect the tripwire area.

Area of Interest (AOI)

Regions defined by users These regions can be monitored and

watched. With this, only the specific area you

want will be tracked which can save on time and space.

Removes unnecessary points. BUT what if those points could be used

and is overlooked?

Cache(ing it all in)

Cache saves time. Cache is used to save time so the disk does not have to

be read. Can get information from disk

Ran from start Can get information from cache

Ran a second time Much faster

In running time, SQL sever can read from cache and disk so it is very good at performance.

AOI does better then Tripwire because of index sizes. Tripwire is larger.

Data Types (Points VS Lines)

Which do you think takes up more space?

This . (point) Or -------- (line) You guessed it! The line! Because of this, when available

points are used and saved instead of lines.

Multiple properties

There are other properties that are used for targets that can be combined to make a search fast. Speed, Size, Classification, Color

Obviously searching for coupled things would result in a faster search. Speed + Size > Speed

Filter breaks down spatial objects, then the properties are taken into account. This trims down on the search time.

Table breakdowns

Tables need to be broken down for easy access. If they were not, then it would take much longer to access all the data.

Table partitions allow for transactions to be searched in specific areas instead of the whole database.

EX time is 30 days which is broken into 3 10 days with all the attributes of time. This makes for a faster search.

The user sees none of this and it is all still 1 table

Database all over

The database isn’t 1 central location. Portioned tables are scattered all

over. A computer may hold a small area

and another may hold more. This may not make sense to a small

region but if this system is for a city, this is a good concept.

Finished

Questions?