Background Modeling and People Tracking
Adaptive Background Modeling for People Detection and Tracking

A robust, real-time multiple people tracker in a cluttered environment is important for video
surveillance systems. A significant part of the task is to accurate segment and robustly tracking
the object under the constraint of low computational complexity.

We focus on two major parts: background modeling and real-time tracking. In order to achieve a
robust background model, a novel background adaptation technique is proposed which yields the
appealing properties of background model generation, model adaptation, and frame level model
group switching. The learned background can model a large variation of background changes, such
as a gradual or sudden illumination change, a moving background, and achieves a better
foreground segmentation result. Efficient computation complexity and memory structure are
simultaneously achieved with this online learning procedure. For human tracking, the learned
background models are naturally and effectively embedded in a probabilistic Bayesian framework
called particle filtering, which allows a multi-object tracking. This adaptive background model
incorporated in a multiple tracking system possesses much greater robustness to problematic
phenomena, without sacrificing real-time performance, making it well-suited for a wide range of
practical applications in human tracking and detection.

1. Edward Andrew Carlson, "dynamic tracking tracking of multiple people", Master Thesis, RPI
2. Andrew Janowczyk,  "Adaptive background modeling for human tracking", Master Thesis, RPI
1. Comparison(click image to enlarge)
True=Ground truth, SSD = Sum of Squared Differene, MG = Mixture of Gaussians, AEM =
Adaptive Expectation Maximization (Our Method)
2. Background modelling and people tracking demo video
Campus Tracking
Video taken from the RPI campus, in real-time we identify the targets
and draw boxes around them
Crossgates Mall
Video taken from the Crossgates Mall, in realtime we identify the targets
and draw boxes around them
Modeling Demo
This output is an attempt at modeling the background of the scene.
The scene starts off in a training mode and then switches over to a
red = background green = foreground mode. The thing to note is that
the cereal box is identified as foreground initially but after a certain
amount of time it is added into the background
Background + particle filtering Demo

We can see that when we go to the red=background,
green=foreground  mode, with the new background modeling
implemented we detect that the cereal box and plant are background
and over time naturally add them in to the scene.
particle filtering)
Here we can see the benefits of the integration of background modeling
with particle filtering. It becomes trivial to track people, even in front of
moving objects such as the fountain.
Light Switch Tracking
Here we can see the benefits of having a background modeler that
allows the scene to be in multiple states. We can easily track a person
even when the light switches from on to off