Title: Vigilant Realtime storage and intelligent retrieval of visual surveillance data Dr Graeme A' Jones
1Vigilant Real-time storage and intelligent
retrieval of visual surveillance dataDr Graeme
A. Jones
V
2Vigilant aims
- to design a database that provides real-time
efficient storage of events occurring within a
monitored scene. - to enable untrained security operators to
generate human-centric queries for video data
i.e. queries based on content.
3System constraints
- Handle video stream in real-time
- Compress the terabyte of digital video generated
per camera per day onto an swappable one gigabyte
disk - Maximum of one high spec PC per camera
- Intuitive button-press query builder
4System architecture
5System architecture
6Fast Robust Event Detection (and tracking)
Algorithm
- Model-based approach exploiting expected
projected object size, with shadow/reflection
7(No Transcript)
8FREDA- Low-level
- Blob detection from pixel comparison with mean
and variance image
- Temporal updating of reference images with soft
gating
9FREDA - High Level
- Region detection from connected components
- Unsupported regions generate new objects
- Current objects validated from supporting regions
10Example Results
11Information Balance Sheet
- Input data
- 800x600 x (10.50.5) x 25 x (60x60x24)
- ? 2Tbytes/day
- Output knowledge
- Periodic background DCT updates
- Subimage sequences (pixels and contour) per
temporal event
12The Intelligent Camera
- Boundary between PC and intelligent camera
depends on issues of frame rate, bandwidth and
computational resources
Pixel Comparsion Connected Components Hypothe
sis Generation Object Validation
Characterisation
13System architecture
14Object Classification
- Object event may be classified into Person,
Vehicle, Large Vehicle classes based on history
of depth-compensated dimensions and speed.
15Object Classification
16Colour Annotation
- Dominant colour(s) of object extracted as modes
from colour histogram generated from pixels of
temporal event.
HUE
Munsell Space. Semantic classification in HSV
colour space
Value
173D Trajectory
- Ground plane calibration (learnt) enables 3D
speed to be computed, and hence velocity
behaviours derived e.g. car maneouvring, person
running, etc. - Trajectory commentary derived from areas of
interest previously assigned labels by operator
e.g. gate, bikeshed, disabled parking.
18083456 Vehicle 3434 enters gate 083503
Vehicle 3434 enters F.Lane 083512 Vehicle 3434
enters carpark 083531 Vehicle 3434 enters
zone3 083605 Vehicle 3434 stops in
zone3 155523 Vehicle 3434 leaves zone3
19Behaviour analysis
- Hidden Markov Models based on states derived from
clusters of positions along training sets of
trajectories used to classify object and its
behaviour
- Car Entering
- Car Leaving
- Person Entering
- Person Leaving
20System architecture
21Conclusions
- Its easy to derive atomic units of useful
user-oriented knowledge - Requirement for human-oriented query
specification tools employing fuzzy matching - Plug n play characteristics e.g. camera
calibration - Distribution of computer intelligence
22Contact Details
- Email g.jones_at_kingston.ac.uk
- Web www.kingston.ac.uk/dirc/