Robust Foreground Detection in Video Using Pixel Layers

Kedar A. Patwardhan, Student Member, IEEE,Guillermo Sapiro, Senior Member, IEEE, and

Vassilios Morellas, Member, IEEE

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL. 30, NO. 4, APRIL 2008

Presented by ：曹憲中

Proposed framework

Kernel Density EstimationWhere K is some kernel and h is a smoothing parameter called the bandwidth.

False Alarm= Type 1 error= False Positives

Initial guess

Training Step

Maximum likelihood Expectation-Maximization (EM) Kernel Density Estimation (KDE) Kullback–Leibler (KL) divergence

original 'baboon' image initial-guess the final layer after the refinement step

Maximum likelihood

最大似然估計是一種統計方法，它用來求一個樣本集的相關機率密度函數的參數。這個方法最早是遺傳學家以及統計學家羅納德 · 費雪爵士在 1912 年至 1922 年間開始使用的。

Expectation-Maximization (EM)

在統計計算中，最大期望（ EM ）演算法是在機率（ probabilistic ）模型中尋找參數 Maximum likelihood 的演算法。最大期望經常用在機器學習和計算機視覺的數據集聚（ Data Clustering ）領域。

Kernel Density Estimation (KDE)

核密度估計，在機率論中用來估計未知的密度函數，屬於非參數檢驗方法之一，由 Rosenblatt (1955) 和 Parsen(1962) 提出， Ruppert 和 Cline 基於數據集密度函數聚類演算法提出修訂的核密度估計方法。

Kullback–Leibler (KL) divergence

Kullback-Leibler Divergence ，是以它的兩個提出者庫爾貝克和萊伯勒的名字命名的。 KL divergence 用來衡量兩個正函數是否相似，對於兩個完全相同的函數，它們的 KL divergence 等於零。在自然語言處理中可以用 KL divergence 來衡量兩個常用詞（在語法上和語義上）是否同義，或者兩篇文章的內容是否相近等等。

            

          

            

          

            

          

            

          

            

          

                 

     

Proposed framework

Kernel Density EstimationWhere K is some kernel and h is a smoothing parameter called the bandwidth.

False Alarm= Type 1 error= False Positives

Online Step

IMPLEMENTATION DETAILS AND EXPERIMENTAL RESULTS

160x120 The algorithm was implemented using C++,

on a machine with Intel-Pentium IV 1.8GHz processor.

In the offline training step, we used an initial training stack of approximately 30 frames for all the results, achieving a running speed of 10 frame/second with our experimental code.

The initial layering and training steps usually require about 5 minutes (for layering all the frames in the initial training stack).

IMPLEMENTATION DETAILS AND EXPERIMENTAL RESULTS

IMPLEMENTATION DETAILS AND EXPERIMENTAL RESULTS

IMPLEMENTATION DETAILS AND EXPERIMENTAL RESULTS

IMPLEMENTATION DETAILS AND EXPERIMENTAL RESULTS

IMPLEMENTATION DETAILS AND EXPERIMENTAL RESULTS

DISCUSSION AND FUTURE SCOPE

In the future, we would like to adapt the framework described here to multicamera scenarios where the different cameras may or may not overlap and also may be of different modalities.

DISCUSSION AND FUTURE SCOPE

The foreground models of moving persons should be made more robust, for example by adding shape information to the global feature-set, toward their use in person identification and tagging throughout the area of surveillance.

REFERENCES Kedar A. Patwardhan, Guillermo Sapiro, Vassilios

Morellas, “A Pixel Layering Framework For Robust Foreground Detection In Video”.

Wikipedia

Jun-Yi Li, “Object Extraction for Video Surveillance System”

Thank you for your attention.