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.

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.

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.

Object-Based Surveillance Video Synopsis Using Genetic Algorithm

2019 ◽  
pp. 857-883
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.

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.

scholarly journals Compressive Imaging of Moving Object Based on Linear Array Sensor

2016 ◽  
Vol 2016 ◽  
pp. 1-6 ◽  
Author(s):  
Changjun Zha ◽  
Yao Li ◽  
Jinyao Gui ◽  
Huimin Duan ◽  
Tailong Xu
Keyword(s):  
Moving Objects ◽  
Linear Array ◽  
Matching Pursuit ◽  
Moving Object ◽  
Imaging Method ◽  
Compressive Imaging ◽  
Array Sensor ◽  
Object Based ◽  
Direct Separation ◽  
Matching Pursuit Algorithm

Using the characteristics of a moving object, this paper presents a compressive imaging method for moving objects based on a linear array sensor. The method uses a higher sampling frequency and a traditional algorithm to recover the image through a column-by-column process. During the compressive sampling stage, the output values of the linear array sensor are multiplied by a coefficient that is a measurement matrix element, and then the measurement value can be acquired by adding all the multiplication values together. During the reconstruction stage, the orthogonal matching pursuit algorithm is used to recover the original image when all the measurement values are obtained. Numerical simulations and experimental results show that the proposed compressive imaging method not only effectively captures the information required from the moving object for image reconstruction but also achieves direct separation of the moving object from a static scene.

The Design and Implementation of Moving Object Detecting Algorithm in Intelligent Video Surveillance

2015 ◽  
Vol 738-739 ◽  
pp. 779-783
Author(s):  
Jin Hua Sun ◽  
Cui Hua Tian
Keyword(s):  
Video Surveillance ◽  
Background Subtraction ◽  
Surveillance System ◽  
Image Sequence ◽  
Background Modeling ◽  
Moving Object ◽  
Video Surveillance System ◽  
Surveillance Video ◽  
Subtraction Method ◽  
Design And Implementation

In view of the problems existed in moving object detection in video surveillance system, background subtraction method is adopted and combined with Surendra algorithm for background modeling, an algorithm of detecting moving object from video is proposed, and OpenCV programming is adopted in Visual c ++ 6.0 for implementation. Experimental results indicate that the algorithm can accurately detect and identify moving object in video by reading the image sequence of surveillance video, the validity of the algorithm is verified.

Study on Tracking in Interested Moving Objects

2011 ◽  
Vol 382 ◽  
pp. 418-421
Author(s):  
Dong Yan Cui ◽  
Zai Xing Xie
Keyword(s):  
Computational Complexity ◽  
Real Time ◽  
Monitoring System ◽  
Moving Objects ◽  
Moving Object ◽  
Experimental Results ◽  
Real Time Monitoring ◽  
Automatic Program ◽  
Inter Frame

This paper presents an automatic program to track in moving objects, using segmentation algorithm quickly and efficiently after the division of a moving object, in the follow-up frame through the establishment of inter-frame vectors to track moving objects of interest. Experimental results show that the algorithm can accurately and effectively track moving objects of interest, and because the algorithm is simple, the computational complexity is small, can be well positioned to meet real-time monitoring system in the extraction of moving objects of interest and tracking needs.

Motion Planning of a Nonholomonic Mobile Manipulator for Moving Object Grasping

2013 ◽  
Vol 380-384 ◽  
pp. 591-594
Author(s):  
Nai Jian Chen ◽  
Fang Zhen Song ◽  
Hong Hua Zhao ◽  
Ying Jun Li
Keyword(s):  
Motion Planning ◽  
Obstacle Avoidance ◽  
Moving Objects ◽  
Nonholonomic Constraints ◽  
Moving Object ◽  
Experimental Results ◽  
Mobile Platform ◽  
Planning System ◽  
Mobile Manipulator

This paper studies a nonholonomic mobile manipulator that consists of a wheeled mobile platform with a mounted serial manipulator. It is designed and developed to navigate autonomously in handling moving objects subjected to nonholonomic constraints. A camera is mounted on the front of the platform and employed to identify and collect information about distance, velocity and direction of the object. With that information collected, a motion planning system determines the preferred operation region and the obstacle-avoidance area, and thus generates the expected trajectory of the mobile manipulator. Experimental results are shown that the mobile manipulator can identify the target, generate trajectories and grasp the moving object autonomously.

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