scholarly journals Motion Direction Inconsistency-Based Fight Detection for Multiview Surveillance Videos

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Chuang Yao ◽  
Xiaoyan Su ◽  
Xuehua Wang ◽  
Xinyi Kang ◽  
Jun Zhang ◽  
...  
Keyword(s):  
Random Forest ◽  
Optical Flow ◽  
Detection Method ◽  
Research Field ◽  
Statistical Characteristics ◽  
Public Security ◽  
Accurate Information ◽  
Cyberphysical Systems ◽  
Motion Direction ◽  
Challenging Research

Nowadays, with the increasing number of surveillance cameras, human behavior detection is of importance for public security. Detection of fight behavior using video surveillance is an essential and challenging research field. We propose a multiview fight detection method based on statistical characteristics of the optical flow and random forest. Cyberphysical systems for monitoring can obtain timely and accurate information from this method. Two novel descriptors named Motion Direction Inconsistency (MoDI) and Weighted Motion Direction Inconsistency (WMoDI) are defined to improve the performance of existing methods for videos with different shooting views and solve the misjudgment on nonfight, such as running and talking. First, YOLO V3 algorithm is applied to mark the motion areas, and then, the optical flow is computed to extract descriptors. Finally, Random Forest is used for classification based on statistical characteristics of descriptors. The evaluation results on CASIA dataset demonstrate that the proposed method can improve the accuracy and reduce the rate of missing alarm and false alarm for the detection, and it is very robust against videos with different shooting views.

scholarly journals A Novel Blind Signal Detector Based on the Entropy of the Power Spectrum Subband Energy Ratio

Entropy ◽  
2021 ◽  
Vol 23 (4) ◽  
pp. 448
Author(s):  
Han Li ◽  
Yanzhu Hu ◽  
Song Wang
Keyword(s):  
Power Spectrum ◽  
Detection Method ◽  
Multinomial Distribution ◽  
Sample Entropy ◽  
Energy Ratio ◽  
Statistical Characteristics ◽  
Interval Probability ◽  
Blind Signal ◽  
Signal Detector ◽  
Subband Energy

In this paper, we present a novel blind signal detector based on the entropy of the power spectrum subband energy ratio (PSER), the detection performance of which is significantly better than that of the classical energy detector. This detector is a full power spectrum detection method, and does not require the noise variance or prior information about the signal to be detected. According to the analysis of the statistical characteristics of the power spectrum subband energy ratio, this paper proposes concepts such as interval probability, interval entropy, sample entropy, joint interval entropy, PSER entropy, and sample entropy variance. Based on the multinomial distribution, in this paper the formulas for calculating the PSER entropy and the variance of sample entropy in the case of pure noise are derived. Based on the mixture multinomial distribution, the formulas for calculating the PSER entropy and the variance of sample entropy in the case of the signals mixed with noise are also derived. Under the constant false alarm strategy, the detector based on the entropy of the power spectrum subband energy ratio is derived. The experimental results for the primary signal detection are consistent with the theoretical calculation results, which proves that the detection method is correct.

scholarly journals Random Forest with Adaptive Local Template for Pedestrian Detection

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Tao Xiang ◽  
Tao Li ◽  
Mao Ye ◽  
Zijian Liu
Keyword(s):  
Computer Vision ◽  
Random Forest ◽  
Classification Accuracy ◽  
Template Matching ◽  
Detection Method ◽  
State Of The Art ◽  
Pedestrian Detection ◽  
Sliding Window ◽  
Experimental Results ◽  
Training Samples

Pedestrian detection with large intraclass variations is still a challenging task in computer vision. In this paper, we propose a novel pedestrian detection method based on Random Forest. Firstly, we generate a few local templates with different sizes and different locations in positive exemplars. Then, the Random Forest is built whose splitting functions are optimized by maximizing class purity of matching the local templates to the training samples, respectively. To improve the classification accuracy, we adopt a boosting-like algorithm to update the weights of the training samples in a layer-wise fashion. During detection, the trained Random Forest will vote the category when a sliding window is input. Our contributions are the splitting functions based on local template matching with adaptive size and location and iteratively weight updating method. We evaluate the proposed method on 2 well-known challenging datasets: TUD pedestrians and INRIA pedestrians. The experimental results demonstrate that our method achieves state-of-the-art or competitive performance.

Network attack detection method for power system terminal based on improved random forest

2021 ◽  
Author(s):  
Tong Yu ◽  
Ming Xie ◽  
Xin Li ◽  
Ying Ling ◽  
Dongmei Bin ◽  
...  
Keyword(s):  
Random Forest ◽  
Power System ◽  
Detection Method ◽  
Attack Detection ◽  
Network Attack

scholarly journals Artificial Ageing of Photocatalytic Nanocomposites for the Protection of Natural Stones

Coatings ◽  
2020 ◽  
Vol 10 (8) ◽  
pp. 729 ◽  
Author(s):  
Marco Roveri ◽  
Sara Goidanich ◽  
Lucia Toniolo
Keyword(s):  
Outdoor Exposure ◽  
Research Field ◽  
Chemical Degradation ◽  
Titania Nanoparticles ◽  
Solar Irradiation ◽  
Accelerated Ageing ◽  
Accurate Information ◽  
Artificial Ageing ◽  
Stone Conservation ◽  
Natural Stones

During the last ten years, photocatalytic nanocomposites combining titania nanoparticles with silicon-based matrices have received increasing attention in the stone conservation research field, because they offer an effective multifunctional approach to the issue of stone protection. However, much work still has to be done in studying the behaviour of these nanocomposites in real environmental conditions and understanding to what extent they are able to retain their effectiveness and compatibility once applied on outdoor surfaces. The latter is a key information that should lie at the basis of any successful conservation and maintenance campaign. The present study provides insight into this relevant topic trough laboratory testing by assessing the artificial ageing of two silane-based photocatalytic nanocomposites, previously selected through an accurate testing on different natural stones. Three accelerated ageing procedures, based on artificial solar irradiation, heating and rain wash-out, allowed simulating about two years of outdoor exposure to some of the weathering factors to which stones are normally subjected. The results provided quite accurate information about the long-term behaviour of the products and on the role that the stone properties play therein. It was shown that, when the products are able to penetrate deeply enough inside the stone pores, they retain much of their hydrophobising and photocatalytic properties and maintain a good compatibility with the stone substrates, even after partial chemical degradation of the alkyl-silica matrices has occurred on the very stone surface.

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