Hasan Thabit Rashid Kurmasha

Object detection, tracking and video analysis are vital and energetic tasks for intelligent video surveillance systems and computer vision applications. Object detection based on background modelling is a major technique used in dynamically objects extraction over video streams. This paper presents the threshold adaptation and XOR accumulation (TAXA) algorithm in three systematic stages throughout video sequences. First, the continuous calculation, updating and elimination of noisy background details with hybrid statistical techniques. Second, thresholds are calculated with an effective mean and Gaussian for the detection of the pixels of the objects. The third is a novel step in making decisions by using XOR-accumulation to extract pixels of the objects from the thresholds accurately. Each stage was presented with practical representations and theoretical explanations. On high resolution video which has difficult scenes and lighting conditions, the proposed algorithm was used and tested. As a result, with a precision average of 0.90% memory uses of 6.56% and the use of CPU 20% as well as time performance, the result excellent overall superior to all the major used foreground object extraction algorithms. As a conclusion, in comparison to other popular OpenCV methods the proposed TAXA algorithm has excellent detection ability.

No reference image quality assessment IQA algorithms are widely used for finding distortions in images without comparing them to references. Image' areas with no details may indicate a brightness saturation change, as well as the presence of noise in the background, which are more visible types of distortions. Thus, the design of such IQA should take into account the human visual conception. This paper proposes a no-reference image quality evaluation algorithm that takes into account the finest edge detection process and entropy deployment in regard to human visual sensitive HVS to quantify brightness saturation variations and noise in pixels. Statistic objective metrics for correlation coefficient CC which person PCC, spearman rank order SROCC, root mean squared error RMSE are used in the objective evaluation in corresponding with the subjective evaluation. The proposed algorithm is tested and significantly has well correlation PCC > 0.87, SROCC > 0.93, RMSE < 0.34. The findings of this research could be used to improve the performance of no-reference HVS-based IQA algorithms currently in use.

Coronavirus (COVID-19) is a contagious disease by SARS-CoV-2 that causes the extreme respiratory disorder. The virus has caused a global crisis that has had repercussions on public health, well-being, and all aspects of public and economic life. Infrastructure, information sources, preventive measures, treatment protocols, and various other resources have been put in place worldwide to combat the growth of this deadly disease. This study used the "AutoRegressive Integrated Moving Average" (ARIMA) forecasting technique to estimate the weekly confirmed cases and deaths from the coronavirus epidemic in Iraq. The data collection period was June 1, 2020, until August 31, 2021. The findings demonstrated the model's high accuracy, with an RMSE of 24.168 for the training data and 32.794 for the testing data.

Image enhancement is a popular technique used for process transferring distortions; contrast-distorted images CDI are treated using different images quality assessment IQA algorithms as full-reference FR-IQA. In this paper, a new FR-IQA is proposed to determine the noise artifacts caused by compression, blurring and other types of distortion as well as the brightness saturation (i.e. no details in the region) which happen for the same reasons above. The proposed FR-IQA is designated based on the variance moment which has the feature of fast computation, very sensitive to represent the dispersion of image pixel intensity (change in details), and very sensitive to the overall brightness of images in correlation to the human visual sensitive HVS. The statistical evaluation outcomes expression that most of the FR-IQAs have humble correlation along with human mean opinion scores MOS although they are using the original image in comparing with the distortion one. The proposed algorithm takes into account the significant properties of human visual perception HVP. The algorithm objective and subjective results have very well scores. It has significantly well correlation for person correlation coefficient PCC > 0.87, spearman rank order SROCC > 0.91 and outlier ratio (OR) equal to 0%.

In this paper, we propose an experimental study of multi-camera collaborative network for surveillance the highway traffic turn in real life scenario, the target of surveillance based offline video processing is capturing the violated vehicles that driving violated paths in many cases specified by user-defined rules. Best topology of the experiment zone is considered and covered by four pillars; each has two (fixed and motorized) cameras that casing the entire specific effective field of view. As to author knowledge, there is no such available experiment, and hence, it could serve researchers that interested in. However, the experiment is done for around 180 recorded hours for 8 videos during 9 days, each video for one camera. It is designed based on the collaborative cameras principle for intelligent video surveillance systems and the outcomes show that the surveillance and the tracking of violated vehicles have been successes in most user-defined rules cases for more than 90 % cases.

Currently, IT develops our life superficially and quickly has become faster and more complicated. However, This paper offers a brief study of previous techniques for violation of vehicles on surveillance systems expressed by suitable processing methodologies to intelligent surveillance techniques (such as Wi-Fi sensors, image processing, machine learning, and object detection based on appearance and motion) and the use of different types of cameras networks(fixed, motorize, and PTZ) and area topologies(efficient FOVs). This study provides a quick look at various techniques that alert individuals and users to vehicle anomalous movements in the environments of traffic and intelligent systems.

Article Info Publish Date 01 Jun 2022 Abstract Object detection, tracking and video analysis are vital and energetic tasks for intelligent video surveillance systems and computer vision applications. Object detection based on background modelling is a major technique used in dynamically objects extraction over video streams. This paper presents the threshold adaptation and XOR accumulation (TAXA) algorithm in three systematic stages throughout video sequences. First, the continuous calculation, updating and elimination of noisy background details with hybrid statistical techniques. Second, thresholds are calculated with an effective mean and gaussian for the detection of the pixels of the objects. The third is a novel step in making decisions by using XOR-accumulation to extract pixels of the objects from the thresholds accurately. Each stage was presented with practical representations and theoretical explanations. On high resolution video which has difficult scenes and lighting conditions, the proposed algorithm was used and tested. As a result, with a precision average of 0.90% memory uses of 6.56% and the use of CPU 20% as well as time performance, the result excellent overall superior to all the major used foreground object extraction algorithms. As a conclusion, in comparison to other popular OpenCV methods the proposed TAXA algorithm has excellent detection ability. International Journal of Electrical & Computer Engineering (2088-8708) Scopus Q2 https://ijece.iaescore.com/index.php/IJECE/article/view/25958