Foggy Port Surveillance Video Clearness Algorithm Based on Wavelet Packet Decomposition

2014 ◽  
Vol 644-650 ◽  
pp. 4616-4619
Author(s):  
Zhi Yuan Xu ◽  
Yong Kai Wang ◽  
Xiao Hong Su ◽  
Yi Liu
Keyword(s):  
Moving Objects ◽  
Wavelet Packet ◽  
Experimental Results ◽  
Wavelet Packet Decomposition ◽  
Surveillance Video ◽  
Surveillance Videos ◽  
Background Image

Port surveillance videos are degraded seriously in foggy conditions. This paper presented a clearness algorithm based on wavelet packet decomposition. Firstly, we extracted the background image from degraded videos and established the updated model; Secondly, we detected the moving objects as foreground images; Thirdly, we defogged these images based on wavelet packet decomposition; Finally, we fused the background and foreground images together. The experimental results show that our method is more effective.

Algorithm of Surveillance Video Synopsis Based on Objects

2013 ◽  
Vol 321-324 ◽  
pp. 1041-1045
Author(s):  
Jian Rong Cao ◽  
Yang Xu ◽  
Cai Yun Liu
Keyword(s):  
Moving Objects ◽  
Background Modeling ◽  
Moving Object ◽  
Experimental Results ◽  
Single Camera ◽  
Surveillance Video ◽  
Video Synopsis ◽  
Object Based ◽  
Camera Surveillance ◽  
Background Image

After background modeling and segmenting of moving object for surveillance video, this paper firstly presented a noninteractive matting algorithm of video moving object based on GrabCut. These matted moving objects then were placed in a background image on the condition of nonoverlapping arrangement, so a frame could be obtained with several moving objects placed in a background image. Finally, a series of these frame images could be achieved in timeline and a single camera surveillance video synopsis could be formed. The experimental results show that this video synopsis has the features of conciseness and readable concentrated form and the efficiency of browsing and retrieval can be improved.

scholarly journals Object-Based Approach for Adaptive Source Coding of Surveillance Video

2019 ◽  
Vol 9 (10) ◽  
pp. 2003 ◽  
Author(s):  
Tung-Ming Pan ◽  
Kuo-Chin Fan ◽  
Yuan-Kai Wang
Keyword(s):  
Linear Model ◽  
Video Transmission ◽  
Moving Objects ◽  
Source Coding ◽  
Video Quality ◽  
Surveillance Video ◽  
Surveillance Videos ◽  
Object Based ◽  
High Recognition Accuracy ◽  
Object Dynamics

Intelligent analysis of surveillance videos over networks requires high recognition accuracy by analyzing good-quality videos that however introduce significant bandwidth requirement. Degraded video quality because of high object dynamics under wireless video transmission induces more critical issues to the success of smart video surveillance. In this paper, an object-based source coding method is proposed to preserve constant quality of video streaming over wireless networks. The inverse relationship between video quality and object dynamics (i.e., decreasing video quality due to the occurrence of large and fast-moving objects) is characterized statistically as a linear model. A regression algorithm that uses robust M-estimator statistics is proposed to construct the linear model with respect to different bitrates. The linear model is applied to predict the bitrate increment required to enhance video quality. A simulated wireless environment is set up to verify the proposed method under different wireless situations. Experiments with real surveillance videos of a variety of object dynamics are conducted to evaluate the performance of the method. Experimental results demonstrate significant improvement of streaming videos relative to both visual and quantitative aspects.

A One-Stop Method for EMI Analysis Based on Wavelet Packet and SOM

2014 ◽  
Vol 530-531 ◽  
pp. 341-344 ◽  
Author(s):  
Hong Yi Li ◽  
Yuan Yang ◽  
Di Zhao
Keyword(s):  
Data Clustering ◽  
Electromagnetic Compatibility ◽  
Wavelet Packet ◽  
Experimental Results ◽  
Wavelet Packet Decomposition ◽  
Feature Map ◽  
Self Organized ◽  
Analysis Process ◽  
Efficiency And Effectiveness ◽  
One Stop

The analysis of electromagnetic interferences (EMI) has been a heated problem in the field of Electromagnetic Compatibility (EMC). As the demand of efficiency and effectiveness is getting higher, the traditional methods have become the short board in analysis process. These methods havent provided a solution to analyze the relation among multiple EMI signals, and the data clustering and mining are currently done manually. To address this problem, in this paper we propose a one-stop method based on the wavelet packet decomposition (WPD) and self-organized feature map (SOM), aiming to provide a systematical and solution to extract and analyze multiple EMI signals. Experimental results are also provided to demonstrate the validity and efficiency of the proposed method.

Fast Background Subtraction Based on GPU

2011 ◽  
Vol 341-342 ◽  
pp. 737-742
Author(s):  
Jian Ping Han ◽  
Xiao Yang Li ◽  
Da Xing Zhang ◽  
Bo Ting Geng
Keyword(s):  
Background Subtraction ◽  
Moving Objects ◽  
Experimental Results ◽  
Surveillance Videos ◽  
Effectiveness And Efficiency ◽  
Asynchronous Execution

In this paper, a fast background subtraction algorithm using codebook model is presented to extract moving objects from surveillance videos. The time for stopped objects being absorbed into the background can be controlled to deal with different applications and have nothing to do with the complexity of the scene. We implement the algorithm on GPU using CUDA, and optimize the implementation using pinned memory and asynchronous execution techniques. Experimental results are provided to demonstrate the accuracy, effectiveness, and efficiency of the proposed algorithm.

scholarly journals Examining the Applicability of Wavelet Packet Decomposition on Different Forecasting Models in Annual Rainfall Prediction

Water ◽  
2021 ◽  
Vol 13 (15) ◽  
pp. 1997
Author(s):  
Hua Wang ◽  
Wenchuan Wang ◽  
Yujin Du ◽  
Dongmei Xu
Keyword(s):  
Wavelet Packet ◽  
Moving Average ◽  
Back Propagation ◽  
Processing Technique ◽  
Annual Rainfall ◽  
Wavelet Packet Decomposition ◽  
Rainfall Forecasting ◽  
Promising Alternative ◽  
Forecasting Models ◽  
Precipitation Prediction

Accurate precipitation prediction can help plan for different water resources management demands and provide an extension of lead-time for the tactical and strategic planning of courses of action. This paper examines the applicability of several forecasting models based on wavelet packet decomposition (WPD) in annual rainfall forecasting, and a novel hybrid precipitation prediction framework (WPD-ELM) is proposed coupling extreme learning machine (ELM) and WPD. The works of this paper can be described as follows: (a) WPD is used to decompose the original precipitation data into several sub-layers; (b) ELM model, autoregressive integrated moving average model (ARIMA), and back-propagation neural network (BPNN) are employed to realize the forecasting computation for the decomposed series; (c) the results are integrated to attain the final prediction. Four evaluation indexes (RMSE, MAE, R, and NSEC) are adopted to assess the performance of the models. The results indicate that the WPD-ELM model outperforms other models used in this paper and WPD can significantly enhance the performance of forecasting models. In conclusion, WPD-ELM can be a promising alternative for annual precipitation forecasting and WPD is an effective data pre-processing technique in producing convincing forecasting models.

scholarly journals Adaptive frequency scaled wavelet packet decomposition for frog call classification

2016 ◽  
Vol 32 ◽  
pp. 134-144 ◽  
Author(s):  
Jie Xie ◽  
Michael Towsey ◽  
Jinglan Zhang ◽  
Paul Roe
Keyword(s):  
Wavelet Packet ◽  
Wavelet Packet Decomposition ◽  
Call Classification

Algorithm of Color Detection for Moving Video Objects Based on Mode Matching

2012 ◽  
Vol 239-240 ◽  
pp. 1000-1003
Author(s):  
Zhao Quan Cai ◽  
Hui Hu ◽  
Tao Xu ◽  
Wei Luo ◽  
Yi Cheng He
Keyword(s):  
Background Subtraction ◽  
Moving Objects ◽  
Color Matching ◽  
Human Beings ◽  
Current Image ◽  
Color Recognition ◽  
Recognition Ability ◽  
Color Identification ◽  
The Difference ◽  
Background Image

It is urgent to study how to effectively identify color of moving objects from the video in the information era. In this paper, we present the color identification methods for moving objects on fixed camera. One kind of the methods is background subtraction that recognizes the foreground objects by compare the difference of pixel luminance between the current image and the background image at the same coordinates. Another kind is based on the statistics of HSV color and color matching which makes the detection more similar to the color identification of the human beings. According to the experiment results, after the completion of the background modelling, our algorithm of background subtraction, statistics of the HSV color and the color matching have strong color recognition ability on the moving objects of video.

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