scholarly journals Visual Analysis and Recognition of Crowd Behavior by Principal Component Analysis

2021 ◽  
Vol 1 (2) ◽  
pp. 99-108
Author(s):  
Hocine Chebi ◽  
Dalila Acheli ◽  
Mohamed Kesraoui
Keyword(s):  
Principal Component Analysis ◽  
Optical Flow ◽  
Human Behavior ◽  
Visual Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Wide Field ◽  
Crowd Behavior ◽  
New Approach ◽  
Field Of Vision

The analysis of the human behavior from video is a wide field of the vision by computer. In this work, we are presenting mainly a new approach and method of detects behavior or abnormal events continuous of crowd in the case of the dangerous situations. These scenes are characterized by the presence of a great number of people in the camera’s field of vision. A major problem is the development of an autonomous approach for the management of a great number of anomalies which is almost impossible to carry out by operators. We present in this paper an approach for the anomalies detection, the visual sequences of the video are detected like behavior normal or abnormal based on the measurement and the extraction of the points by the optical flow, then calculations of the distance between the matrices of covariance of the distributions of the vectors of movement calculated on the consecutive reinforcements.

scholarly journals Scaled PCA: A New Approach to Dimension Reduction

2021 ◽  
Author(s):  
Dashan Huang ◽  
Fuwei Jiang ◽  
Kunpeng Li ◽  
Guoshi Tong ◽  
Guofu Zhou
Keyword(s):  
Principal Component Analysis ◽  
Supervised Learning ◽  
Principal Component ◽  
Real Data ◽  
Component Analysis ◽  
General Factor ◽  
Macroeconomic Forecasting ◽  
New Approach ◽  
Learning Technique ◽  
The Common

This paper proposes a novel supervised learning technique for forecasting: scaled principal component analysis (sPCA). The sPCA improves the traditional principal component analysis (PCA) by scaling each predictor with its predictive slope on the target to be forecasted. Unlike the PCA that maximizes the common variation of the predictors, the sPCA assigns more weight to those predictors with stronger forecasting power. In a general factor framework, we show that, under some appropriate conditions on data, the sPCA forecast beats the PCA forecast, and when these conditions break down, extensive simulations indicate that the sPCA still has a large chance to outperform the PCA. A real data example on macroeconomic forecasting shows that the sPCA has better performance in general.

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