The Recognition of Human Movement Using Temporal Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Fall Detection from Human Shape and Motion History Using Video Surveillance
AINAW '07 Proceedings of the 21st International Conference on Advanced Information Networking and Applications Workshops - Volume 02
Video-Based Human Movement Analysis and Its Application to Surveillance Systems
IEEE Transactions on Multimedia
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Visual recognition of human fall incidents in video clips has been an active research issue in recent years, However, most published methods cannot effectively differentiate between fall-down and fall-like incidents such as sitting and squatting. In this paper, we present a novel shadow-assistant method for detecting human fall. Normally, complex 3-D models are used to estimate the human height. However, to reduce the high computational cost, only the information of moving shadow is used for this context. Because the system is based on a combination of shadow-assistant height estimation, and a cascade of SVM classifiers, it can distinguish between fall-down and fall-like incidents with a high degree of accuracy from very short sequence of 1-10 frames. Our experimental results demonstrate that under bird's-eye view camera setting, the proposed system still can achieve 100% detect rate and a low false alarm rate, while the detection rate of other fall detection schemes have been dropped dramatically.