Technologies for Building Intelligent Video Surveillance Systems and Methods for Background Subtraction in Video Sequences

2021 ◽  
pp. 468-480
Author(s):  
Anatolii Babaryka ◽  
Ivan Katerynchuk ◽  
Oksana Komarnytska
Keyword(s):  
Video Surveillance ◽  
Background Subtraction ◽  
Video Sequences ◽  
Surveillance Systems ◽  
Intelligent Video Surveillance

VIDEO-BASED APPROACH FOR DETECTING PROHIBITED ACTIVITIES ON SPORTING COURTS

2013 ◽  
Vol 13 (04) ◽  
pp. 1350019
Author(s):  
SHUENN-JYI WANG ◽  
CHUNG-KAI HSIEH ◽  
TSORNG-LIN CHIA
Keyword(s):  
Video Surveillance ◽  
Background Subtraction ◽  
Moving Objects ◽  
Activity Analysis ◽  
Video Sequences ◽  
Surveillance Systems ◽  
Activity Detection ◽  
Second Stage ◽  
Subtraction Procedure ◽  
Surveillance Cameras

Video surveillance cameras are ubiquitous this decade. With the popularization of sports, costly courts are built widespread. To protect the sport ground against from damage, some activities, such as biking and in-line skating, are prohibited in the court. Traditional video surveillance systems can not prevent these activities in time. In this paper, we propose a video-based approach for detecting prohibited activities that can damage a court. The approach involves prohibited activity analysis and prohibited activity detection. The first stage generates the leg-angle curves of a prohibited activity. A background subtraction procedure is applied to extract moving objects; moving objects are then normalized to minimize influences of various scaling conditions. Leg-angle curves of prohibited activities are generated by computing leg angles. The second stage detects specific prohibited activities by analyzing leg-angle curves obtained from the input video sequences. Our proposed method can detect the prohibited activities in a court, thus preventing such activities in time.

Abnormal Motion Detection for Intelligent Video Surveillance

2011 ◽  
Vol 58-60 ◽  
pp. 2290-2295 ◽  
Author(s):  
Ruo Hong Huan ◽  
Xiao Mei Tang ◽  
Zhe Hu Wang ◽  
Qing Zhang Chen
Keyword(s):  
Kalman Filter ◽  
Object Tracking ◽  
Video Surveillance ◽  
Motion Detection ◽  
Background Subtraction ◽  
Moving Objects ◽  
Video Streams ◽  
Intelligent Video Surveillance ◽  
Tracking Process ◽  
Object Parameters

A method of abnormal motion detection for intelligent video surveillance is presented, which includes object intrusion detection, object overlong stay detection and object overpopulation detection. Background subtraction algorithm is used to detect moving objects in video streams. Kalman filter is applied for object tracking. By the construction of relation matrix, the tracking process is divided into five statuses for prediction and estimation, which are object disappearing, object separating, new object appearing, object sheltering and object matching. The object parameters and predictive information in the next frame which is used to track moving objects is established by Kalman filter. Then, three types of abnormal motion detection are implemented. The relative position of alarm area or guard line with the rectangle boxes of the moving objects is used to detect whether the object is invading. The existing time of the moving objects in monitor area is counted to detect whether the object is staying too long. Moving objects in the monitor area are classified and counted to detect whether the objects are too much. Alarm will be triggered when abnormal motion detection as defined is detected in the monitor area.

scholarly journals Efficient zero-copy mechanism for intelligent video surveillance networks

2018 ◽  
Vol 189 ◽  
pp. 03021
Author(s):  
Shiyan Chen ◽  
Dagang Li
Keyword(s):  
Video Surveillance ◽  
Transmission Delay ◽  
Surveillance Systems ◽  
Hardware Support ◽  
Surveillance Networks ◽  
Intelligent Video Surveillance ◽  
Resource Limited ◽  
Zero Copy ◽  
Simulation Results

Most of today’s intelligent video surveillance systems are based on Linux core and rely on the kernel’s socket mechanism for data transportation. In this paper, we propose APRO, a novel framework with optimized zero-copy capability customized for video surveillance networks. Without the help of special hardware support such as RNIC or NetFPGA, the software-based APRO can effectively reduce the CPU overhead and decrease the transmission latency, both of which are much appreciated for resource-limited video surveillance networks. Furthermore, unlike other software-based zero-copy mechanisms, in APRO zero-copied data from network packets are already reassembled and page aligned for user-spac applications to utilize, which makes it a ‘true’ zero-copy solution for localhost applications. The proposed mechanism is compared with standard TCP and netmap, a typical zero-copy framework. Simulation results show that APRO outperforms both TCP and localhost optimized netmap implementation with the smallest transmission delay and lowest CPU consumption.

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