HCI 期末報告 Foreground Motion Detection By

Difference Based Spatial-Temporal Entropy Image

陳威男 P76961455林姿妤 P76961031曾瑞瑜 P76964259

孫程 CSIE98 F74978067

指導教授盧文祥老師高宏宇老師鄭憲宗老師

OutlineIntroductionProblem definitionPaper's method

Spatial –temporal Entropy ImageDifference-based Spatial Temporal Entropy Image

ImplementationExperiment result

IntroductionA novel human motion detection method

based on entropy is shown in this paper ,it is motivated by other’s previous work.

Methods by others and by the author will be introduced later.

Problem definition Human motion detection method based on

entropy is faced with.

Entropy from motion and spatial diversity is very hard to differentiate.

Paper's methodSpatial-Temporal Entropy Image

Difference-based Spatial Temporal Entropy Image

Spatial-Temporal Entropy ImageEntropy: a measure of uncertaintyNoise: camera channel noise and noise

brought by flickering of lightMotion: motion object

Pixel’s state change by noise would be in a small range, but those by motion will be large.

So the diversity of state at each pixel can be used to characterize the intensity of motion at its position.

Spatial-Temporal Entropy ImageW*W*L histogramPixels used to accumulate histogram for (i,j)

Spatial-Temporal Entropy ImageOnce the histogram is obtained the corresponding

density function for each pixel can be computed by:

N : the total number of pixel in the histogramq : the bins of the histogramQ : the total number of bins and

Eij is called spatial-temporal entropy of pixel (i,j)

Q

qqji

p1

,,1

Spatial-Temporal Entropy ImagePixels at edges get higher entropy.Both motion and spatial diversity can cause

high entropy and they are very hard to differentiate .

This point can lead a false detection of motion.

Difference-based Spatial Temporal Entropy ImageForm histogram by accumulate pixels in different

imagesImage noises are Gaussian distributed

No motion: Pixels occurs would follows zero-mean Gaussian distributions in several difference images

Motion occurs: Pixels used to form the histogram would have higher value in difference images

Pixel would distributed in a wide rangeSo entropy obtained this way can denote the

intensity of motion.

Step1: Calculate difference imagesRGB color images are first converted to 256

gray level images, denoted by F(k), where k is the frame number.

The difference image D(k) is calculated by (3) in which | | denote absolute value, and ‧ Φ( ) is ‧the quantization function.

In all our experiments, Φ( ) quantizes the 256 ‧gray levels into Q=20 gray levels.

Step2: Histogram accumulationHistogram Hi,j(q) for pixel(i,j) is updated

online using simple recursive updates.Specifically, the first L frames, i.e. from D(1)

to D(L) are used to accumulate histogram for D(L) using (4):

And histograms for the subsequent frames are updated by:• Where the constant α is set empirically to

control the influence of history frames.

Step3: Obtain DSTEIEach pixel at the kth framethe pdf for pixel(i,j) is formed by normalizing

the histogram using (6):• Where Γ( ) is the normalization function.‧

In this paper, the pixel number in each bin of Hi,j,k is divided by the total number of pixels in the histogram.

Pi,j,k has 20 discrete values.After pdf for each pixel is obtained, entropy is

calculated using (7):

Step4: Motion object locationAfter obtain the DSTEI image ,many methods

can be used to derive the motion region.One way is to find the peak of it then apply

region growing method to locate the motion object.

In this paper, another simple method is simply to binarized the DSTEI image by a threshold T.

Implementation(1/3)計算 |F(k)-F(k-1)|=| 這個frame pixel 的 RGB- 上個 frame pixel 的 RGB|

D(k)=Φ(|F(k)-F(k-1)|):256 gray levels →

20 gray levels

判斷 D(k) 落在1~20 哪個 bin 中

累積每個 pixel 算出來的D(k) 落在每個 bin 的值

If > σ(i,j)pixel 加入標

記

Count<5

Count>=5

Implementation(2/3)|F(k)-F(k-1)|=| 這個 frame pixel 的 RGB- 上個 frame pixel 的

RGB|

D(k)=Φ(|F(k)-F(k-1)|): 256 gray levels →20 gray levels

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Implementation(3/3)初始值 :

If > σ, pixel(i,j) 加入標記

Experimental Result(1/4) 執行環境 : .Net (visual studio 2005)Code: C#CPU: Core2 Quad Q6600 2.4GHzRAM : 2GB

Experimental Result(2/4) 物體由左向右移 :

Experimental Result(3/4) 物體由左向右移 :

Experimental Result(4/4)