Real-time performance evaluation of BGSLibrary algorithms for intelligent surveillance

<p>Background subtraction is the first and basic stage in video analysis and smart surveillance to extract moving objects. In fact, the background subtraction library (BGSLibrary) was created by Andrews Sobral in 2012, which currently combines 43 background subtraction algorithms from the most popular and widely used in the field of video analysis. Each algorithm has its own characteristics, strengths and weaknesses in extracting moving objects. The evaluation allows the identification of these characteristics and helps researchers to design the best methods. Unfortunately, the literature lacks a comprehensive evaluation of the algorithms included in the library. Accordingly, the present work will evaluate these algorithms in the BGSLibrary through the segmentation performance, execution time and processor, so as to, achieve a perfect, comprehensive, real-time evaluation of the system. Indeed, a background modeling challenge (BMC) dataset was selected using the synthetic video with the presence of noise. Results are presented in tables, columns and foreground masks.</p>

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Real Time Tracking RGB Color Based Kinect

Modern Applied Science ◽

10.5539/mas.v11n3p98 ◽

2017 ◽

Vol 11 (3) ◽

pp. 98

Author(s):

Ahmed Mustafa Taha Alzbier ◽

Hang Cheng

Keyword(s):

Computer Vision ◽

Real Time ◽

Video Processing ◽

Background Subtraction ◽

Moving Objects ◽

Blue Color ◽

The Real ◽

Color Recognition ◽

Kinect Camera ◽

Computer Vision Technology

As the present computer vision technology is growing up, and the multiple RGB color object tracking is considered as one of the important tasks in computer vision and technique that can be used in many applications such as surveillance in a factory production line, event organization, flow control application, analysis and sort by colors and etc. In video processing applications, variants of the background subtraction method are broadly used for the detection of moving objects in video sequences. The background subtraction is the most popular and common approach for motion detection. However , this is paper presents our investigation the first objective of the whole algorithm chain is to find the RGB color within a video. The idea from the beginning was to look for certain specific features of the patches, which would allow distinguishing red, green and blue color objects in the image. In this paper an algorithm is proposed to track the real time moving RGB color objects using kinect camera. We will use a kinect camera to capture the real time video and making an image frame from this video and extracting red, green and blue color .Here image processing is done through MATLAB for color recognition process each color. Our method can tracking accurately at 95% in real-time.

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Asynchronous Semantic Background Subtraction

Journal of Imaging ◽

10.3390/jimaging6060050 ◽

2020 ◽

Vol 6 (6) ◽

pp. 50

Author(s):

Anthony Cioppa ◽

Marc Braham ◽

Marc Van Droogenbroeck

Keyword(s):

Real Time ◽

Background Subtraction ◽

Semantic Information ◽

Moving Objects ◽

Temporal Evolution ◽

Semantic Segmentation ◽

Feedback Mechanism ◽

Segmentation Algorithm ◽

Frame Rate ◽

The One

The method of Semantic Background Subtraction (SBS), which combines semantic segmentation and background subtraction, has recently emerged for the task of segmenting moving objects in video sequences. While SBS has been shown to improve background subtraction, a major difficulty is that it combines two streams generated at different frame rates. This results in SBS operating at the slowest frame rate of the two streams, usually being the one of the semantic segmentation algorithm. We present a method, referred to as “Asynchronous Semantic Background Subtraction” (ASBS), able to combine a semantic segmentation algorithm with any background subtraction algorithm asynchronously. It achieves performances close to that of SBS while operating at the fastest possible frame rate, being the one of the background subtraction algorithm. Our method consists in analyzing the temporal evolution of pixel features to possibly replicate the decisions previously enforced by semantics when no semantic information is computed. We showcase ASBS with several background subtraction algorithms and also add a feedback mechanism that feeds the background model of the background subtraction algorithm to upgrade its updating strategy and, consequently, enhance the decision. Experiments show that we systematically improve the performance, even when the semantic stream has a much slower frame rate than the frame rate of the background subtraction algorithm. In addition, we establish that, with the help of ASBS, a real-time background subtraction algorithm, such as ViBe, stays real time and competes with some of the best non-real-time unsupervised background subtraction algorithms such as SuBSENSE.

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Automatic Vehicles Detection, Classification and Counting Techniques / Survey

Iraqi Journal of Science ◽

10.24996/ijs.2020.61.7.30 ◽

2020 ◽

pp. 1811-1822

Author(s):

Mustafa Najm ◽

Yossra Hussein Ali

Keyword(s):

Real Time ◽

Intelligent Transportation Systems ◽

Background Subtraction ◽

Moving Objects ◽

Vehicle Detection ◽

Intelligent Transportation ◽

Traffic Monitoring ◽

Transportation Systems ◽

Traffic Condition ◽

Real Time Processing

Vehicle detection (VD) plays a very essential role in Intelligent Transportation Systems (ITS) that have been intensively studied within the past years. The need for intelligent facilities expanded because the total number of vehicles is increasing rapidly in urban zones. Trafﬁc monitoring is an important element in the intelligent transportation system, which involves the detection, classification, tracking, and counting of vehicles. One of the key advantages of traffic video detection is that it provides traffic supervisors with the means to decrease congestion and improve highway planning. Vehicle detection in videos combines image processing in real-time with computerized pattern recognition in flexible stages. The real-time processing is very critical to keep the appropriate functionality of automated or continuously working systems. VD in road traffics has numerous applications in the transportation engineering field. In this review, different automated VD systems have been surveyed, with a focus on systems where the rectilinear stationary camera is positioned above intersections in the road rather than being mounted on the vehicle. Generally, three steps are utilized to acquire traffic condition information, including background subtraction (BS), vehicle detection and vehicle counting. First, we illustrate the concept of vehicle detection and discuss background subtraction for acquiring only moving objects. Then a variety of algorithms and techniques developed to detect vehicles are discussed beside illustrating their advantages and limitations. Finally, some limitations shared between the systems are demonstrated, such as the definition of ROI, focusing on only one aspect of detection, and the variation of accuracy with quality of videos. At the point when one can detect and classify vehicles, then it is probable to more improve the flow of the traffic and even give enormous information that can be valuable for many applications in the future.

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Smart Video Surveillance Systems and Identification of Human Behavior Analysis

Countering Cyber Attacks and Preserving the Integrity and Availability of Critical Systems - Advances in Digital Crime, Forensics, and Cyber Terrorism ◽

10.4018/978-1-5225-8241-0.ch004 ◽

2019 ◽

pp. 64-97

Author(s):

M. Sivabalakrishnan ◽

R. Menaka ◽

S. Jeeva

Keyword(s):

Real Time ◽

Video Surveillance ◽

Background Subtraction ◽

Surveillance System ◽

Surveillance Systems ◽

Cloud Environment ◽

Background Estimation ◽

Security Officers ◽

Smart Surveillance ◽

Human Behavior Analysis

Smart surveillance cameras are placed in many places such as bank, hospital, toll gates, airports, etc. To take advantage of the video in real time, a human must monitor the system continuously in order to alert security officers if there is an emergency. Besides, for event detection a person can observe four cameras with good accuracy at a time. Therefore, this requires expensive human resources for real-time video surveillance using current technology. The framework of ATM video surveillance system encompassing various factors, such as image acquisition, background estimation, background subtraction, store, and further process like segmentation, people counting, and tracking are done in cloud environment briefly discussed in this chapter.

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