Multi-CAMSHIFT應用於多角度人臉追蹤與辨識
Multi-CAMSHIFT for Multi-View Faces Tracking and Recognition
Date Issued
2005
Date
2005
Author(s)
Lin, Chun-Ting
DOI
en-US
Abstract
This thesis, aims to develop a system for multiple objects tracking and multi-view faces detection and recognition. We propose a novel method (Multi-CAMSHIFT), which is based on the characteristics of color and shape probability distribution, to solve the tracking problems for multiple objects. The tracker is used to get the candidate regions by outlining the interested probability distribution. The system performance is further improved by using multi-resolution framework and computation reduction. The principal component analysis (PCA) and support vector machine (SVM) are integrated to form the multi-view faces detection and recognition module for classifying different face poses and identities. Beside color information, the gray background image is used to locate the human head in the region of tracking pedestrian based on probability distribution rule. The rule can also be used for skin color face tracking to remove background region (non-face region).
Since the proposed Multi-CAMSHIFT (MCAMSHIFT) is computationally efficient, it can work in complex background and track in real-time. The slowly changing lighting condition is effectively resolved using probability model update. From experiments, the proposed MCAMSHIFT was successfully applied to multi-view faces tracking and recognition. It can also be applied to surveillance system, pedestrian tracking and face guard systems.
Subjects
人臉
追蹤
偵測
辨識
多角度人臉
主成分分析
支持向量機
face
tracking
detection
recognition
multi-view
PCA
SVM
Type
thesis
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