NVIDIA Unveils Multi-Camera Tracking Workflow for Large Space Management

Large
spaces
such
as
warehouses,
factories,
stadiums,
and
airports
often
rely
on
numerous
cameras
to
ensure
safety
and
streamline
operations.
However,
managing
and
accurately
tracking
objects
across
multiple
camera
feeds
can
be
challenging.
To
address
these
complexities,
NVIDIA
has
introduced
a
new
multi-camera
tracking
reference
workflow,
aimed
at
enhancing
the
efficiency
of
vision
AI
systems
in
monitoring
and
managing
large
spaces,
according
to
the

NVIDIA
Technical
Blog.

NVIDIA
Multi-Camera
Tracking

The
newly
announced
NVIDIA
multi-camera
tracking
workflow
offers
a
customizable
starting
point
for
developers,
significantly
reducing
development
time.
This
workflow
includes
state-of-the-art
AI
models
trained
on
real
and
synthetic
datasets,
along
with
real-time
video
streaming
modules.
Key
components
of
the
workflow
include:

• 
  Foundation
  layer:
  Fuses
  multi-camera
  feeds
  to
  create
  global
  IDs
  for
  objects,
  along
  with
  their
  global
  and
  local
  coordinates.
• 
  Analytics
  layer:
  Provides
  unique
  object
  counts
  and
  local
  trajectories.
• 
  Visualization
  and
  UI:
  Includes
  sample
  heatmaps,
  histograms,
  and
  pathing
  that
  can
  be
  further
  customized.

These
components
enable
developers
to
build
end-to-end
vision
AI
applications
tailored
to
their
specific
business
needs.

Challenges
in
Multi-Camera
Tracking

Implementing
multi-camera
tracking
systems
can
be
complex
due
to
several
factors:

• 
  Subject
  matching:
  Advanced
  algorithms
  and
  AI
  models
  are
  required
  to
  accurately
  match
  subjects
  across
  multiple
  camera
  feeds
  from
  different
  angles
  and
  views.
  Training
  these
  models
  can
  take
  months
  due
  to
  the
  need
  for
  extensive
  ground-truth
  datasets.
• 
  Real-time
  requirements:
  Real-time
  multi-camera
  tracking
  necessitates
  specialized
  modules
  for
  live
  data
  streaming,
  multi-stream
  fusion,
  behavior
  analytics,
  and
  anomaly
  detection,
  all
  delivering
  subsecond
  latency
  and
  high
  throughput.
• 
  Scalability:
  Scaling
  these
  systems
  to
  large
  spaces
  like
  factories
  or
  airports
  requires
  distributed
  computing
  and
  a
  cloud-native
  architecture
  capable
  of
  handling
  thousands
  of
  cameras
  and
  subjects.

Getting
Started
with
the
Multi-Camera
Tracking
Workflow

For
those
interested
in
deploying
this
workflow,
NVIDIA
provides
a

quickstart
guide
that
details
how
to
deploy
the
reference
workflow
on
local
development
environments
or
in
the
cloud.
Additionally,
the

end-to-end
Sim2Deploy
recipe
offers
further
guidance
on
simulating
and
fine-tuning
the
workflow
for
specific
use
cases.

End-to-End
Workflow
for
Multi-Camera
Tracking

The
multi-camera
tracking
reference
workflow
processes
live
or
recorded
streams
from
the
Media
Management
microservice,
outputting
the
behavior
and
global
IDs
of
objects
in
the
multi-camera
view.
Object
metadata,
including
bounding
boxes,
tracking
IDs,
and
behavior
data
with
timestamps,
is
stored
in
an
Elasticsearch
index
and
a
Milvus
vector
database.
A
Web
UI
microservice
allows
users
to
visualize
behaviors
and
track
objects
over
time.

For
example,
in
the
provided

Figure
1,
the
right
pane
shows
a
building
map
with
the
global
ID
of
an
object
and
its
behavior,
while
the
left
pane
displays
the
current
location
of
the
object.
Users
can
query
objects
by
their
global
IDs
to
track
their
movements
across
the
camera
network.

Building
and
Deploying
the
Multi-Camera
Tracking
Workflow

NVIDIA
offers
multiple
options
for
building
and
deploying
the
multi-camera
tracking
application:

• 
  Quick
  deployment
  with
  Docker
  Compose:
  NVIDIA
  provides
  sample
  video
  streams
  and
  perception
  metadata,
  allowing
  users
  to
  deploy
  the
  end-to-end
  workflow
  with
  a
  simple
  Docker
  Compose
  command.
• 
  Production
  deployment
  with
  Kubernetes:
  Detailed
  instructions
  are
  available
  for
  deploying
  the
  application
  in
  Kubernetes,
  including
  Helm
  charts
  and
  configuration
  files.
• 
  Cloud
  deployment:
  NVIDIA
  provides
  one-click
  deployment
  scripts
  for
  various
  cloud
  service
  providers,
  including
  Microsoft
  Azure,
  Google
  Cloud
  Platform,
  and
  Amazon
  Web
  Services
  (AWS).

Monitoring
and
Logging

The
multi-camera
tracking
application
integrates
with
the
Kibana
dashboard,
allowing
users
to
monitor
and
visualize
application
performance.
The
dashboard
provides
insights
into
object
detection,
unique
object
counts,
and
multi-camera
tracking
workflows
over
time.

In

Figure
2,
the
Kibana
dashboard
displays
behavior
histograms
and
the
number
of
unique
objects
across
camera
streams,
providing
a
comprehensive
view
of
the
tracked
entities.

Conclusion

The
NVIDIA
multi-camera
tracking
reference
workflow
is
now
available
in
developer
preview,
offering
a
robust
solution
for
managing
and
optimizing
large
spaces.
Developers
can
get
started
by
following
the

quickstart
guide
and
deploying
the
workflow
in
their
environment.
For
further
customization
and
development,
NVIDIA
provides
comprehensive
tools
and
documentation.

Image
source:
Shutterstock

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