A Statistical Approach to Culture Colors Distribution in Video Sensors Angela D Angelo, Jean-Luc Dugelay

Transcription

1 A Statistical Approach to Culture Colors Distribution in Video Sensors Angela D Angelo, Jean-Luc Dugelay VPQM 2010, Scottsdale, Arizona, U.S.A, January 13-15

2 Outline Introduction Proposed approach Colors Database Visual Analysis Fuzzy cluster Conclusion

3 Introduction Color is an important cue in the distinction and recognition of objects Object recognition and identification Image retrieval Skin detection Color recognition in video surveillance Identifying the surface color of an object in a video surveillance system is critical video sensors illumination conditions objects distances from the camera objects orientations

4 Proposed work Analysis of the performance of color spaces in identifying colors in camera networks Robust color identification tool Track a given person across the field-of-view of multiple cameras Localize a missing child in a crowded amusement park Related works Color constancy algorithms Color invariant models

5 Color constancy algorithms Color constancy is the ability of HVS to describe colors in spite of variations in illumination conditions Recover a descriptor for each surface in a scene as it would be seen under a canonic illuminant taken as a reference. Common assumptions Single camera Uniform scene illumination Frontal surface orientation Spatially-distributed surveillance cameras operating under different lighting conditions and with varying color sensitivity

6 Color invariant models Color spaces invariant to illumination variations Many color spaces and distance metrics have been introduced in the scientific literature Most of the paper does not provide a comparison of the existing color spaces Acceptable results on limited database with almost any color space Experimental results linked to a specific application Skin color detection

7 Culture colors The goal of the proposed work is to learn how colors can drift in different illumination conditions and with different color spaces Color selection Culture colors (black, white, red, yellow, green, blue, brown, purple, pink, orange, grey) The idea is to collect pixels corresponding to the culture colors, in real illumination conditions

8 Mining testbed Colors database Associate each culture color to a sport team Randomly collect video clips of the selected teams and collect pixels from them 4/5 video clips for each team, around 120 pixels for each color

9 Mining testbed 1355 pixel samples real illumination conditions different positiond of the objects with respect to illumination different cameras 5 widely used color spaces: RGB, normalized RGB, HSV, Lab, YUV Analysis of the colors distributions in the 5 color spaces

10 Colors distributions

11 Colors distributions: HSV color space Mettere i grafici dei piani hv hs

12 Fuzzy clustering Fuzzy clustering is suited to color quantization since color boundaries are not well defined Fuzzy k-nearest neighbors algorithm (Keller & al.) Training set of m samples Z 1, Z 2,, Z m X vector to be classified Fixed a value of k and C (possible classes) Find among Z the k nearest neighbors to X: Y 1, Y 2,,Y k Find the membership vector of X by combining the membership vectors of Y New dataset of 1104 samples k j m j k j m j ij i Y X Y X w X u j i ij Y w u

13 True Positive Rate True Positive Rate True Positive Rate True Positive Rate True Positive Rate Experimental results ROC - RGB color space ROC - Normalized RGB color space ROC - HSV color space False Positive Rate ROC - Lab color space False Positive Rate ROC - YUV color space False Positive Rate RGB Norm RGB HSV Lab YUV Accuracy False Positive Rate False Positive Rate

14 Lab confusion matrix

15 Conclusions New approach to provide a comparison of the color spaces in describing and identifying colors in video Ad-hoc dataset Visual analysis of colors distributions Fuzzy clustering applied to 5 color spaces Development of robust color detection framework

16 Thanks for the attention!