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An Unsupervised Approach for
Segmentation and Clustering of
Soccer
P. Spagnolo, N.Mosca, M.Nitti, A.Distante
Adviser : Chih-Hung Lin
Speaker : Kuan-Ju Chen
Date
: 2009/05/05
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Contents
1
Introduction
2
Method
3
Experimental results
4
Conclusions
2
Introduction
Image segmentation and classification are
keytechnologies for object-based image
processing.
sport applications of computer vision are
increasing in many context
 Tennis
 Football
 Golf
Many works focus on soccer applications
 Most popular sports around the world
 Broadcasted on television
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Introduction
The applications are based on structural
procedures:
 The player segmentation
 Tracking
 Classification
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Introduction
The most used algorithms for moving objects
detection are based on background subtraction:
 the foreground objects are extracted by subtracting
the current image from a reference background model.
I  B  thoushold
-
=
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Introduction
For the player classification:
 Manually initialized and then, for each player
provided by the segmentation step
 use color histograms to characterized
playersand referees
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Introduction
In the this paper, firstly the background
model is presented then, the feature
extraction and classification steps are
explained in detail.
Finally, the experimental results obtained
on real image sequences acquired during
soccer matches of the Italian Serie A are
reported
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Method
START
Player
Segmentation
Feature
Extraction
Classification
Over
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Method
Step1.Statistic each point energy for few
frames
Step2.Compute the mean to build
backgroud B
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Method
Step 3.Use background subtraction to get
foreground.
E ( x, y )  I x, y   B( x, y )  th
t
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Method
START
Player
Segmentation
Feature
Extraction
Classification
Over
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Method
Player
Histograms in the RGB color space
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Method
Manhattan distance :
1
d1 ( x, y )   wi xi  yi
i 1
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Method
START
Player
Segmentation
Feature
Extraction
Classification
Over
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Method
Find the minimum distance foreground
If the distanch > th and m<q
Create a new cluster
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Experimental results
Test Sequence
A
B
C
D
E
Training Set
Ground Truth
3
2
3
3
2
Detected
clusters
3
2
3
2
2
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Processing
time (ms)
127
112
107
140
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Experimental results
The spatial distribution of feature vectors for
the test sequence C-1
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Experimental results
Test
sequence
Player Team A
Player Team B
Referee
DR
FAR
DR
FAR
DR
FAR
A-1
94.55
2.45
98.33
3.11
93.72
4.33
C-1
93.34
3.55
97.61
2.98
96.16
3.07
C-2
98.22
4.45
92.35
4.65
94.83
4.28
D-1
82.21
TP 3.09 78.26FP 9.03
8.83DR 91.22

FAR 
E-2
93.50
5.21
TP  FN
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97.23
3.43
TP  FP
95.26
4.81
Experimental results
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Conclusion
We have tested this procedure also on
standard PAL video sequences
broadcasted on television.
 Algorithms have worked well
For future work,
 information about the spatial distribution of
color instead of the simple spectral
distribution information typical of histograms.
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Manhattan Distance
 曼哈頓距離(Manhattan Distance )
 我們可以定義曼哈頓距離的正式意義為L1-距離或城市區塊距離，也就是在歐幾裡德空
間的固定直角坐標系上兩點所形成的線段對軸產生的投影的距離總和。
 例如在平面上，座標（x1, y1）的點P1與座標（x2, y2）的點P2的曼哈頓距離為︰

|x1-x2|+|y1-y2|
 要注意的是，曼哈頓距離倚賴座標系統的轉度，而非系統在座標軸上的平移或映射。
 曼哈頓距離的命名原因是從規劃為方型建築區塊的城市（如曼哈頓）間，最短的行車
路徑而來（忽略曼哈頓的單向車道以及只存在於3、14大道的斜向車道）。任何往東三
區塊、往北六區塊的的路徑一定最少要走九區塊，沒有其他捷徑。
 計程車幾何學滿足除了SAS全等定理之外的希伯特定理，SAS全等指任兩個三角型兩個
邊與一個角相等，則這兩個三角型必全等。
 在計程車幾何學中，一個圓是由從園心向各個固定曼哈頓距離標示出來的點圍成的區
域。因此這種圓其實就是旋轉了45度的正方形。如果有一群圓，任兩圓皆相交，則整
群圓必在某點相交；因此曼哈頓距離會形成一個超凸度量空間（Injective metric space）。
對一個半徑為r 的圓來說，這個正方形的圓每邊長√2r。此'"圓"的半徑r對切比雪夫距離
（L∞空間）的二維平面來說，也是一個對座標軸來說邊長為2r的正方形，因此二維切
比雪夫距離可視為等同於旋轉且放大過的二維曼哈頓距離。然而這種介於L1與L∞的相
等關係並不能延伸到更高的維度。
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