Detecting Moving Objects, Ghosts, and Shadows in Video Streams Rita Cucchiara, Costantino Grana, Massimo Piccardi and Andrea Prati Adviser : Chih-Hung Lin Speaker : Kuan-Ju Chen Date 　　 : 2009/02/19

Author Author R. Cucchiara, C. Grana, and A. Prati are with the Dipartimento di Ingegneria dell’Informazione, Universita` di Modena e Reggio Emilia, Via Vignolese, 905/b, 41100 Modena, Italy. . M. Piccardi is with Department of Computer Systems, Faculty of IT, University of Technology, Sydney, Broadway NSW 2007, Australia. Accept IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL. 25, NO. 10, OCTOBER 2003 revised 23 Sept. 2002; accepted 25 Feb.2003

DETECTING MOVING OBJECTS Contents INTRODUCTION 1 DETECTING MOVING OBJECTS 2 SHADOW DETECTION 3 RESULTS 4 CONCLUSIONS 5

INTRODUCTION Detection of moving objects in video streams is the first relevant step of information extraction in many computer vision applications Video surveillance People tracking Video surveillance Video監視

INTRODUCTION In this work, the models of the target objects and their motion are unknown the most widely adopted approach for moving object detection with fixed camera is based on background subtraction 模型的目標物件和他的動作都是未知的， 最廣泛採用的方法的運動目標檢測與固定相機是基於背景減除

INTRODUCTION Background Subtraction Two problems Accurately Reactivity If the background model is neither accurate nor reactive Ghosts Shadows

INTRODUCTION Solution: Statistical combination Combine Combination frames to compute the background model Combine Combine the current frame and previous models with recursive filtering to update the background model

INTRODUCTION In this paper, we propose a novel simple method that exploits all these features, combining them so as to efficiently provide detection of moving objects, ghosts, and shadows. The main contribution the integration of knowledge of detected objects, shadows, and ghosts in the segmentation process enhance object segmentation and background update. 的主要貢獻 整合知識的被測物體，陰影，和鬼的分割進程 加強目標分割和背景更新。

DETECTING MOVING OBJECTS The aim Detect real moving objects. Avoiding detection of transient spurious objects Sakbot (Statistical And Knowledge-Based ObjecT detection) Statistics and knowledge of the segmented objects to improve both background modeling and moving object detection 1.偵測真實移動物體 避免虛假檢測瞬態物體 2.為了實現這些目標，我們提出一個分類對象的興趣，現場

DETECTING MOVING OBJECTS MVO (Moving visual object) set of connected points belonging to object characterized by nonnull motion Ghost a set of connected points detected as in motion by means of background subtraction, but not corresponding to any real moving object Shadow a set of connected background points modified by a shadow cast over them by a moving object set of connected points belonging to object characterized by nonnull motion 一套連接點屬於object的特點是nonnull運動 一套檢測為連接點的方式在運動的背景減法，而不是任何實際的對應移動物體 一組連接點修改背景的陰影在他們的移動物體

DETECTING MOVING OBJECTS Taxonomy

DETECTING MOVING OBJECTS Frame t background model Foreground

DETECTING MOVING OBJECTS Get a statistical information Follow set of the statistical background model knowledge-based background model Using the median function get a statistical information: If p belong to MVO , p=background model If p dosen`t belong to MVO, p= statistical background model

DETECTING MOVING OBJECTS used to update the background model to update background model If p isn`t belong to known object , p= If p is belong to known object , p= update background model

SHADOW DETECTION Mean the process of classification of foreground pixels as “shadow points” based on their appearance

SHADOW DETECTION Foreground Ayalyze Hue-Saturation-Value(HSV) color space Following three condition to mask shadow identifying as shadows those points define a darkening effect of shadows shadow mask: average image luminance

RESULTS The reactivity of the background model. 反應性的背景模型。第一欄包含了背景模型框架＃ 65 。第二列包含一個縮放的詳細幀 ＃ 100 （上圖）和檢測MVOs （黑色）採用像素只有選擇性（下圖） ：誤報的原因是鬼。第三欄的報告 檢測MVO使用Sakbot （上） ，並利用統計背景更新只在幀＃ 134 （下）

RESULTS simply statistical background pixel-level selective background statistical and knowledge-based background models Segmentation is provided via background subtraction including shadow detection. (a) false positives and (b) false negatives.

CONCLUSIONS This paper has presented Sakbot, a system for moving object detection in image sequences This system has the unique characteristic of explicitly addressing various troublesome situations such as cast shadows and ghosts. Cast shadows are detected and removed from the background update function Ghosts are also explicitly modeled and detected so as to avoid a further cause of undesired background modification The method is highly computationally cost-effective since it is not severe in computational time 本文提出了Sakbot ，系統的移動物體 檢測圖像序列中。 該系統具有獨特的 特點明確解決各種棘手的情況下 如蒙上陰影和鬼。 蒙上陰影檢測 並從後台更新功能， Ghost的定義 以避免多餘的背景更新 計算方法是非常符合成本效益 因為它沒有嚴重的計算時間

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DETECTING MOVING OBJECTS MVOs are validated by applying a set of rules on area, saliency, and motion as follows: The MVO blob must be large enough The MVO blob must be a “salient” foreground blob The MVO blob must have nonnegligible motion

DETECTING MOVING OBJECTS Validation rules

Follow set of S the statistical background model: knowledge-based background model: update the background model: