Chris Cummings

 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?

 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)

 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.

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

 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.

 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.

 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

 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.

 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.

 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.

 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 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.

 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.

 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.

 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

 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.

 Questions?