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We present in this paper an intelligent video surveillance system to detect human fall incidents for enhanced safety in indoor environments. The system consists of two main parts: a vision component which can reliably detect and track moving people in the view of a camera, and an event-inference module which parses observation sequences of people features(More)
In this paper, we present a novel approach to counting number of people that pass the view of an overhead mounted camera. Moving people are first detected as blobs and represented by binary masks, based on which possible multi-person blobs are further segmented into isolated persons according to their areas and locations. Each single person is tracked(More)
Detecting changes in video scenes is of fundamental importance for various video surveillance tasks. Of particular interest are abnormal changes of foreground human behaviors/activities that could pose damages or dangers to human properties and lives. In this paper, we propose a unified sequential approach to detecting, as soon as possible, human fall(More)
This paper presents a new method for counting the number of persons from video images. Conventional people counting methods can be classified into the learning-tracking based techniques. They either require elaborated human model learned using AdaBoost or sophisticated tracking algorithms by particle filtering. The proposed algorithm performs people(More)
In this paper, we propose a novel approach to automatic detection and clustering of human faces presented in videos. In each video shot, continuously appearing human faces are firstly associated to form face sequences. Instead of matching the face sequences directly, we partition them into subsequences consisting of similar poses for the ease of comparison.(More)
Human face has emerged as a useful feature in recent advances in semantic-based video analysis. Due to the large variation of facial poses in videos, conventional face recognition methods for authentication and identification generally experience difficulties in this particular application domain. Based on the recently proposed affinity propagation(More)
We propose a novel probabilistic reasoning approach to recognizing people entering and leaving a closed room in the work place or living environment. In our method people are represented using a low-level color feature, based on which optimal recognition is carried out by exploiting the temporal correlation among the sequence of observations of people.(More)
In this paper, we present a novel approach to accurate detection and tracking of human faces in videos. The idea is to propagate the information of a group of seed faces detected off-line with high certainties to recover the faces undetected or detected with only low confidence. Specifically, our approach first estimates from the color of the seed faces a(More)
By taking the first example for implementation of CCS project by a China power corporation, this article provides the analysis of implementation methods, challenges and benefits of international packaged customer service and marketing management software in China in detail from aspects such as implementation planning of CCS system, methodology, technical(More)