scholarly journals Efficient FPGA Implementation of Human Detection from Video Sequences

2020 ◽  
Vol 9 (6) ◽  
pp. 242-248
Keyword(s):  
Background Subtraction ◽  
Moving Objects ◽  
Median Filter ◽  
Region Of Interest ◽  
Video Stream ◽  
Human Detection ◽  
Video Sequences ◽  
Computer Vision Applications ◽  
And Performance ◽  
Better Than

Detection of Human is a vital and difficult task in computer vision applications like a police investigation, vehicle tracking, and human following. Human detection in video stream is very important in public security management. In such security related cases detecting an object in the video, sequences are very important to understand the behavior of moving objects which normally used in the background subtraction technique. The input data is preprocessed using a modified median filter and Haar transform. The region of interest is extracted using a background subtraction algorithm with remaining spikes removed using threshold technique. The proposed architecture is coded using standard VHDL language and performance is checked in the Spartan-6 FPGA board. The comparison result shows that the proposed architecture is better than the existing method in both hardware and image quality

scholarly journals Background Subtraction (BS) Using Instant Pixel Histogram

2019 ◽  
Vol 54 (5) ◽  
Author(s):  
Narjis Mezaal Shati ◽  
Sundos Abdulameer Alazawi ◽  
Huda Abdulaali Abdulbaqi
Keyword(s):  
Computer Vision ◽  
False Alarm ◽  
False Alarm Rate ◽  
Background Subtraction ◽  
Detection Rate ◽  
Moving Objects ◽  
Moving Objects Detection ◽  
Computer Vision Applications ◽  
Objects Detection ◽  
Published Research

Video computer vision applications require moving objects detection as a first phase of their operation. Therefore, background subtraction (BS), an investigate branch in computer vision with intensive published research, is applied to obtain the “background” and the “foreground.” Our study proposes a new BS model that utilizes instant pixel histogram, which is implemented to extract foreground objects from two datasets, the first Visor (different human actions) and the second Anomaly Detection Dataset UCSD (Peds2). The model when using the Visor dataset gives 100% detection rate with 8% false alarm rate, whereas, when using UCSD (Peds2), it achieves a detection rate and false alarm rate of 77% and 34% respectively.

scholarly journals Compressive Background Modeling for Foreground Extraction

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Yong Wang ◽  
Qian Lu ◽  
Dianhong Wang ◽  
Wei Liu
Keyword(s):  
Background Subtraction ◽  
Background Modeling ◽  
Video Sequences ◽  
Computationally Efficient ◽  
Subtraction Method ◽  
Foreground Extraction ◽  
Image Frame ◽  
Computer Vision Applications ◽  
Background Image ◽  
Classical Background

Robust and efficient foreground extraction is a crucial topic in many computer vision applications. In this paper, we propose an accurate and computationally efficient background subtraction method. The key idea is to reduce the data dimensionality of image frame based on compressive sensing and in the meanwhile apply sparse representation to build the current background by a set of preceding background images. According to greedy iterative optimization, the background image and background subtracted image can be recovered by using a few compressive measurements. The proposed method is validated through multiple challenging video sequences. Experimental results demonstrate the fact that the performance of our approach is comparable to those of existing classical background subtraction techniques.

scholarly journals An Improved Mixture-of-Gaussians Background Model with Frame Difference and Blob Tracking in Video Stream

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Li Yao ◽  
Miaogen Ling
Keyword(s):  
Moving Objects ◽  
Color Space ◽  
Video Stream ◽  
Background Model ◽  
Foreground Extraction ◽  
Mixture Of Gaussians ◽  
Frame Difference ◽  
Rgb Color Space ◽  
Computer Vision Applications ◽  
Blob Tracking

Modeling background and segmenting moving objects are significant techniques for computer vision applications. Mixture-of-Gaussians (MoG) background model is commonly used in foreground extraction in video steam. However considering the case that the objects enter the scenery and stay for a while, the foreground extraction would fail as the objects stay still and gradually merge into the background. In this paper, we adopt a blob tracking method to cope with this situation. To construct the MoG model more quickly, we add frame difference method to the foreground extracted from MoG for very crowded situations. What is more, a new shadow removal method based on RGB color space is proposed.

scholarly journals Real-Time Eyeblink Detector and Eye State Classifier for Virtual Reality (VR) Headsets (Head-Mounted Displays, HMDs)

Sensors ◽  
2019 ◽  
Vol 19 (5) ◽  
pp. 1121 ◽  
Author(s):  
Nassr Alsaeedi ◽  
Dieter Wloka
Keyword(s):  
Virtual Reality ◽  
Real Time ◽  
Motion Vector ◽  
Region Of Interest ◽  
Video Stream ◽  
Detection Algorithm ◽  
Case Scenario ◽  
Current Frame ◽  
Eyelid Closure ◽  
And Performance

The aim of the study is to develop a real-time eyeblink detection algorithm that can detect eyeblinks during the closing phase for a virtual reality headset (VR headset) and accordingly classify the eye’s current state (open or closed). The proposed method utilises analysis of a motion vector for detecting eyelid closure, and a Haar cascade classifier (HCC) for localising the eye in the captured frame. When the downward motion vector (DMV) is detected, a cross-correlation between the current region of interest (eye in the current frame) and a template image for an open eye is used for verifying eyelid closure. A finite state machine is used for decision making regarding eyeblink occurrence and tracking the eye state in a real-time video stream. The main contributions of this study are, first, the ability of the proposed algorithm to detect eyeblinks during the closing or the pause phases before the occurrence of the reopening phase of the eyeblink. Second, realising the proposed approach by implementing a valid real-time eyeblink detection sensor for a VR headset based on a real case scenario. The sensor is used in the ongoing study that we are conducting. The performance of the proposed method was 83.9% for accuracy, 91.8% for precision and 90.40% for the recall. The processing time for each frame took approximately 11 milliseconds. Additionally, we present a new dataset for non-frontal eye monitoring configuration for eyeblink tracking inside a VR headset. The data annotations are also included, such that the dataset can be used for method validation and performance evaluation in future studies.

Background Subtraction Based on Gaussian Mixture Model

2013 ◽  
Vol 694-697 ◽  
pp. 2021-2026
Author(s):  
De Fang Liu ◽  
Ming Deng ◽  
Dai Mu Wang
Keyword(s):  
Probability Density ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Background Subtraction ◽  
Adaptive Algorithm ◽  
Moving Objects ◽  
Gaussian Mixture ◽  
Video Sequences ◽  
Recursive Equations ◽  
Gauss Mixture

According to the detection of moving objects in video sequences, the paper puts forward background subtraction based on Gauss mixture model. It analyzes the usual pixel-level approach, and to develop an efficient adaptive algorithm using Gaussian mixture probability density. Recursive equations are used to constantly update the parameters and but also to simultaneously select the appropriate number of components for each pixel.

scholarly journals Efficient Motion Detection Algorithm in Video Sequences

2014 ◽  
Vol 13 (3) ◽  
pp. 4329-4334 ◽  
Author(s):  
Aree Ali Mohammed
Keyword(s):  
Motion Detection ◽  
Background Subtraction ◽  
Detection Algorithm ◽  
Human Detection ◽  
Video Sequences ◽  
Background Information ◽  
Morphological Operations ◽  
Shadow Removal ◽  
Subtraction Technique ◽  
Objects Detection

Human  motion  analysis  concerns  the detection,  tracking  and  recognition  of  people  behaviors,  from image  sequences  involving  humans. A  reference  frame  is  initially  used  and  considered  as  background  information. While a new object enters into the frame, the foreground information and background information are identified using the reference frame as background model.In this paper, an efficient algorithm is proposed for objects detection in real time video sequences. The method aims at tracking an object like (human) in motion using background subtraction technique. The tracked objects are subject to different pre and post image processing in order to extract the most important features (frame preprocessing is used for shadow removal using morphological operations). They can be used later for recognition in different security system such as (human detection for surveillance). Experimental results show that the proposed method is very efficient in terms of reliability and accuracy of detection.

scholarly journals Method of Moving Region Detection for Static Camera

2018 ◽  
Vol 12 (6) ◽  
pp. 3626-3633
Author(s):  
Pravesh Kumar Goel ◽  
Paresh P. Kotak ◽  
Amit Gupta
Keyword(s):  
Computer Vision ◽  
Object Detection ◽  
Background Subtraction ◽  
Video Sequence ◽  
Moving Objects ◽  
Moving Object Detection ◽  
Region Detection ◽  
Computer Vision Applications ◽  
Object Center ◽  
Background Image

The moving object detection from a stationary video sequence is a primary task in various computer vision applications. In this proposed system; three processing levels are suppose to perform: detects moving objects region from the background image; reduce noise from the pixels of detected region and extract meaningful objects and their features (area of object, center point of area etc.). In this paper; background subtraction techniques is used for segments moving objects from the background image, which is capable for pixel level processing. Morphology operation (Erosion and dilation) are used to remove pixel to pixel noise. In last level, CCL algorithm is used for sorts out foregrounds pixels are grouped into meaningful connected regions and their features.

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.

scholarly journals Background Modelling using a Q-Tree Based Foreground Segmentation

2020 ◽  
Vol 21 (1) ◽  
pp. 17-31
Author(s):  
S Shahidha Banu ◽  
N Maheswari
Keyword(s):  
Background Subtraction ◽  
Moving Objects ◽  
False Negative ◽  
Region Of Interest ◽  
Intensity Variation ◽  
Experimental Results ◽  
Foreground Segmentation ◽  
Foreground Detection ◽  
Foreground Object ◽  
Background Modelling

Background modelling is an empirical part in the procedure of foreground mining of idle and moving objects. The foreground object detection has become a challenging phenomenon due to intermittent objects, intensity variation, image artefact and dynamic background in the video analysis and video surveillance applications. In the video surveillances application, a large amount of data is getting processed by everyday basis. Thus it needs an efficient background modelling technique which could process those larger sets of data which promotes effective foreground detection. In this paper, we presented a renewed background modelling method for foreground segmentation. The main objective of the work is to perform the foreground extraction only inthe intended region of interest using proposed Q-Tree algorithm. At most all the present techniques consider their updates to the pixels of the entire frame which may result in inefficient foreground detection with a quick update to slow moving objects. The proposed method contract these defect by extracting the foreground object by controlling the region of interest (the region only where the background subtraction is to be performed) and thereby reducing the false positive and false negative. The extensive experimental results and the evaluation parameters of the proposed approach with the state of art method were compared against the most recent background subtraction approaches. Moreover, we use challenge change detection dataset and the efficiency of our method is analyzed in different environmental conditions (indoor, outdoor) from the CDnet2014 dataset and additional real time videos. The experimental results were satisfactorily verified the strengths and weakness of proposed method against the existing state-of-the-art background modelling methods.

scholarly journals Object Detection and Tracking in Discriminant Subspace

2017 ◽  
Vol 7 (3) ◽  
pp. 62
Author(s):  
Amith. R ◽  
V.N. Manjunath Aradhya
Keyword(s):  
Background Subtraction ◽  
Moving Objects ◽  
Dimensional Space ◽  
Complex Environments ◽  
Dynamic Changes ◽  
One Dimensional ◽  
Detection And Tracking ◽  
Tracking Process ◽  
Challenging Research ◽  
Computer Vision Applications

<div><p>Detection and tracking of moving objects in video is essential for many computer vision applications and it is considered as a challenging research issue due to dynamic changes in background, illumination, object size and shape. Many traditional algorithms fails to detect and track the moving objects accurately, this paper proposes a robust method, to detect and track moving objects based on the combination of background subtraction and Orthogonalized Fisher’s Discriminant (OFD). Background subtraction detects the foreground objects on subtracting frame by frame basis and updating the background model recursively. Orthogonalized Fisher’s Discriminant projects high dimensional data onto a one dimensional space with the highest recognizability, which speedup the detection and tracking process and also preserves the structure of the objects resulting high accuracy. The proposed method is tested on standard datasets with complex environments and experimental results obtained are encouraging.</p></div>

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