scholarly journals USING THE DISCRETE HADAMARD TRANSFORM TO DETECT MOVING OBJECTS IN SURVEILLANCE VIDEO

2009 ◽  
Keyword(s):  
Moving Objects ◽  
Hadamard Transform ◽  
Surveillance Video

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.

Object-Based Surveillance Video Synopsis Using Genetic Algorithm

2017 ◽  
pp. 193-219
Author(s):  
Shefali Gandhi ◽  
Tushar V. Ratanpara
Keyword(s):  
Genetic Algorithm ◽  
Background Subtraction ◽  
Moving Objects ◽  
Time Lapse ◽  
Original Video ◽  
Surveillance Video ◽  
Time Activity ◽  
Video Synopsis ◽  
Object Based ◽  
Poisson Image Editing

Video synopsis provides representation of the long surveillance video, while preserving the essential activities of the original video. The activity in the original video is covered into a shorter period by simultaneously displaying multiple activities, which originally occurred at different time segments. As activities are to be displayed in different time segments than original video, the process begins with extracting moving objects. Temporal median algorithm is used to model background and foreground objects are detected using background subtraction method. Each moving object is represented as a space-time activity tube in the video. The concept of genetic algorithm is used for optimized temporal shifting of activity tubes. The temporal arrangement of tubes which results in minimum collision and maintains chronological order of events is considered as the best solution. The time-lapse background video is generated next, which is used as background for the synopsis video. Finally, the activity tubes are stitched on the time-lapse background video using Poisson image editing.

Research of AForge.NET in Motion Video Detection

2014 ◽  
Vol 496-500 ◽  
pp. 2150-2153 ◽  
Author(s):  
Cheng Ying Gong ◽  
Hui He
Keyword(s):  
Moving Objects ◽  
Detection Method ◽  
Threshold Value ◽  
Moving Target ◽  
Location Tracking ◽  
Surveillance Video ◽  
Subtraction Method ◽  
Optical Flow Method ◽  
Adjacent Frame ◽  
Motion Video

In general, moving target detection method has the background subtraction method,the adjacent frame difference method and the optical flow method. In order to quickly and accurately detect the moving targets of surveillance video , expand the location tracking study.This paper describes the detection method in real-time vehicle moving target,used AForge.NET library and based on the absolute of image gray difference is greater than the taken threshold value. Implementation results show that this method can effectively and quickly detect moving objects in video sequences.

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.

scholarly journals Detection and Recognition of Abnormal Running Behavior in Surveillance Video

2012 ◽  
Vol 2012 ◽  
pp. 1-14 ◽  
Author(s):  
Ying-Ying Zhu ◽  
Yan-Yan Zhu ◽  
Wen Zhen-Kun ◽  
Wen-Sheng Chen ◽  
Qiang Huang
Keyword(s):  
Moving Objects ◽  
Tracking Algorithm ◽  
Multitarget Tracking ◽  
Surveillance Video ◽  
Criminal Cases ◽  
Time Performance ◽  
Intersection Area ◽  
Spatiotemporal Parameters ◽  
Abnormal Behaviors ◽  
Detection And Recognition

Abnormal running behavior frequently happen in robbery cases and other criminal cases. In order to identity these abnormal behaviors a method to detect and recognize abnormal running behavior, is presented based on spatiotemporal parameters. Meanwhile, to obtain more accurate spatiotemporal parameters and improve the real-time performance of the algorithm, a multitarget tracking algorithm, based on the intersection area among the minimum enclosing rectangle of the moving objects, is presented. The algorithm can judge and exclude effectively the intersection of multitarget and the interference, which makes the tracking algorithm more accurate and of better robustness. Experimental results show that the combination of these two algorithms can detect and recognize effectively the abnormal running behavior in surveillance videos.

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.

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