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.

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.

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.

Multi Object Detection and Tracking from Video File

2014 ◽  
Vol 533 ◽  
pp. 218-225 ◽  
Author(s):  
Rapee Krerngkamjornkit ◽  
Milan Simic
Keyword(s):  
Computer Vision ◽  
Background Subtraction ◽  
Moving Objects ◽  
Gaussian Mixture Models ◽  
Gaussian Mixture ◽  
Video Tracking ◽  
Partial Occlusion ◽  
Video File ◽  
Detection And Tracking ◽  
Over Time

This paper describes computer vision algorithms for detection, identification, and tracking of moving objects in a video file. The problem of multiple object tracking can be divided into two parts; detecting moving objects in each frame and associating the detections corresponding to the same object over time. The detection of moving objects uses a background subtraction algorithm based on Gaussian mixture models. The motion of each track is estimated by a Kalman filter. The video tracking algorithm was successfully tested using the BIWI walking pedestrians datasets [. The experimental results show that system can operate in real time and successfully detect, track and identify multiple targets in the presence of partial occlusion.

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.

Multi-object-based Vessel Traffic Scheduling Optimisation in a Compound Waterway of a Large Harbour

2018 ◽  
Vol 72 (3) ◽  
pp. 609-627 ◽  
Author(s):  
Xinyu Zhang ◽  
Ruijie Li ◽  
Xiang Chen ◽  
Junjie Li ◽  
Chengbo Wang
Keyword(s):  
Genetic Algorithm ◽  
Traffic Flow ◽  
Simulation Experiment ◽  
Case Analysis ◽  
Slot Allocation ◽  
Traffic Scheduling ◽  
Multi Objective Genetic Algorithm ◽  
Object Based ◽  
Traffic Conflicts ◽  
Optimisation Model

In order to investigate the benefits of compound waterways more fully, this study reveals vessel navigational mode and traffic conflicts in a compound waterway through a case analysis, following which a type of simplified prototype of a compound waterway is proposed and three key conflict areas are specified. Based on the three key sub-models of slot allocation for vessels in a waterway entrance, traffic flow conversion of a main and auxiliary waterway in a precautionary area, and traffic flow coordination of division and confluence in a Y crossing area, a vessel traffic scheduling optimisation model is presented, with the minimum waterway occupancy time and minimum total waiting time of vessels as the objective. Furthermore, a multi-objective genetic algorithm is proposed to solve the model and a simulation experiment is carried out. By analysing the optimised solution and comparing it with other scheduling schemes in common use, the results indicate that this method can effectively improve navigation safety and efficiency in a compound waterway.

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