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 METHOD OF IMAGE PROCESSING IN THE PROBLEM OF DETECTING MOVING OBJECTS IN OPTICAL-ELECTRONIC SURVEILLANCE SYSTEMS OF THERMAL IMAGING TYPE

Doklady BGUIR ◽  
2020 ◽  
Vol 18 (2) ◽  
pp. 96-104
Author(s):  
E. I. Mikhnionok
Keyword(s):  
Image Processing ◽  
False Alarm ◽  
Background Subtraction ◽  
Thermal Imaging ◽  
Surveillance System ◽  
Moving Objects ◽  
Field Of View ◽  
Surveillance Systems ◽  
Electronic Surveillance ◽  
Noise Component

The article considers the method of image processing proposed by the author in relation to the problem of automatic detection of moving objects in optoelectronic thermal imaging systems. Moving objects on the observed scene are subject to investigation, so it is advisable to use algorithms based on background subtraction methods to solve the detection problem. However, the observed objects may include objects of interest (a person, a vehicle), as well as other objects and background elements that increase the noise component of the observed situation. Also, the increase in the noise component is greatly influenced by false segmentation in the foreground of the areas of processed images when transferring the field of view of the sensor of the optical-electronic surveillance system. The purpose of this article is to prove the reduction of the probability of false alarm of an automatic detector due to the author's proposed approaches to image processing. The research uses the mathematical apparatus of probability theory and simulation with subsequent statistical processing of data. The article shows that the probability of a false alarm of an automatic detector based on the background subtraction method increases significantly after the transfer of the field of view of the sensor of the optical-electronic surveillance system and decreases after the movement stops as the areas of the processed image that are falsely highlighted in the foreground are automatically segmented. The simulation showed that the approaches proposed by the author can increase the peak signal-to-noise ratio of processed images and reduce the probability of a false alarm of the automatic detector of objects of interest. The results obtained show the feasibility of adapting detection algorithms based on background subtraction methods to work in scanning optoelectronic surveillance systems.

scholarly journals Traffic extreme situations detection in video sequences based on integral optical flow

2019 ◽  
Vol 43 (4) ◽  
pp. 647-652 ◽  
Author(s):  
H. Chen ◽  
S. Ye ◽  
A. Nedzvedz ◽  
O. Nedzvedz ◽  
H. Lv ◽  
...  
Keyword(s):  
Optical Flow ◽  
Video Surveillance ◽  
Real World ◽  
Road Traffic ◽  
Video Sequences ◽  
Surveillance Systems ◽  
Directional Movement ◽  
Background Motion ◽  
Random Background ◽  
Integral Optical Flow

Road traffic analysis is an important task in many applications and it can be used in video surveillance systems to prevent many undesirable events. In this paper, we propose a new method based on integral optical flow to analyze cars movement in video and detect flow extreme situations in real-world videos. Firstly, integral optical flow is calculated for video sequences based on optical flow, thus random background motion is eliminated; secondly, pixel-level motion maps which describe cars movement from different perspectives are created based on integral optical flow; thirdly, region-level indicators are defined and calculated; finally, threshold segmentation is used to identify different cars movements. We also define and calculate several parameters of moving car flow including direction, speed, density, and intensity without detecting and counting cars. Experimental results show that our method can identify cars directional movement, cars divergence and cars accumulation effectively.

scholarly journals Object extraction in video sequences based on spatiotemporal independent component analysis

2021 ◽  
Author(s):  
Zhenhe Chen
Keyword(s):  
Independent Component Analysis ◽  
Video Processing ◽  
Moving Objects ◽  
Research Work ◽  
Region Growing ◽  
Component Analysis ◽  
Independent Component ◽  
Video Sequences ◽  
Object Extraction ◽  
Surveillance Systems

Video object extration is one of the most important areas of video processing in which objects from video sequences are extracted and used for many applications such as surveillance systems, pattern recognition etc. In this research work, an object-based technique based on the spatiotemporal independent component analysis (stICA) is developed to extract moving objects from video sequences. Using the stICA, the preliminary source images containing moving objects in the video sequence are extracted. These images are processed using wavelet analysis, edge detection, region growing and multiscale segmentation techniques to improve the accuracy of the extracted objects. A novel compensation method is applied to deal with the nonlinear problem caused by the application of the stICA directly to the video sequences. The recovered objects are indexed by the singular calue decompensation (SVD) and linear combination analysis. Simulation results demonstrate the effectiveness of the stICA-based object extraction technique in content-based video processing applications.

scholarly journals Features-Based Moving Objects Tracking for Smart Video Surveillances: A Review

2018 ◽  
Vol 27 (02) ◽  
pp. 1830001 ◽  
Author(s):  
Nor Nadirah Abdul Aziz ◽  
Yasir Mohd Mustafah ◽  
Amelia Wong Azman ◽  
Amir Akramin Shafie ◽  
Muhammad Izad Yusoff ◽  
...  
Keyword(s):  
Object Detection ◽  
Video Surveillance ◽  
Surveillance System ◽  
Moving Objects ◽  
Visual Surveillance ◽  
Surveillance Systems ◽  
Objects Tracking ◽  
Object Appearance ◽  
Human Operators ◽  
Active Research

Video surveillance is one of the most active research topics in the computer vision due to the increasing need for security. Although surveillance systems are getting cheaper, the cost of having human operators to monitor the video feed can be very expensive and inefficient. To overcome this problem, the automated visual surveillance system can be used to detect any suspicious activities that require immediate action. The framework of a video surveillance system encompasses a large scope in machine vision, they are background modelling, object detection, moving objects classification, tracking, motion analysis, and require fusion of information from the camera networks. This paper reviews recent techniques used by researchers for detection of moving object detection and tracking in order to solve many surveillance problems. The features and algorithms used for modelling the object appearance and tracking multiple objects in outdoor and indoor environment are also reviewed in this paper. This paper summarizes the recent works done by previous researchers in moving objects tracking for single camera view and multiple cameras views. Nevertheless, despite of the recent progress in surveillance technologies, there still are challenges that need to be solved before the system can come out with a reliable automated video surveillance.

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