PCV Lab3

Background Subtraction

Background Subtraction is a key element in many algorithms used in

the context of image sequence analysis. The distinction between image

background and foreground is, for instance, a meaningful cue for the

detection and tracking of pedestrians in video surveillance.

The goal of this exercise is to obtain an advanced understanding of the

approaches for background subtraction presented in the lectures. The

exercise involves the modelling of the background and the decision for

every pixel whether it belongs to the foreground or to the background.

The background model to be used for this exercise is the "Single

Gaussian" model.

Given data:

 Image sequence of a surveillance camera in a station

Programming Task:

 Implementation of the sequential estimation for the parameters

of a single Gaussian (mean and variance).

 Implementation of the background/foreground labelling process

for every pixel using the variance of the background model

(threshold at 2.5σ).

 Simple implementation of a noise reduction in the labels using

morphological operations

 Evaluate the given image sequence with two different values for

the learning rate 1 2 ,where  1/50, 1/1400

Questions to be answered in written report:

 Apply the implementation of the background subtraction

algorithm using a single Gaussian as a background model to the

given image sequence. Display and watch the results to get an

impression of the effects that can be observed. Describe the

results you got after applying your implementation to the given

image sequence concisely. Observations about your results,

e.g., how results change with different learning rates, in which

part of the image wrong labelling occurs more often, are also

required to be summarized.

 Chose two meaningful image frames for which you show

a) the background mean image,

b) the background variance image,

c) the difference image of mean background and current frame,

d) the binary foreground/background mask

for a learning rate 1/50. Indicate the frame numbers in the caption of the figure.

 Name the advantages and drawbacks of the single Gaussian model.

 Discuss the result of your implemented method when facing difficult cases (consider shadows, reflections, people/things standing still, etc.) and interpret the reason for 1 or 2 cases where pixels are wrongly labelled.