Erssa Arif^1,*, Syed Khuram Shahzad², Rehman Mustafa¹, Muhammad Arfan Jaffar³, Muhammad Waseem Iqbal⁴

Intelligent Automation & Soft Computing, Vol.32, No.2, pp. 909-922, 2022, DOI:10.32604/iasc.2022.021061

Abstract The conventional surveillance and control system of Closed-Circuit Television (CCTV) cameras require human resource supervision. Almost all the criminal activities take place using weapons mostly handheld gun, revolver, or pistol. Automatic gun detection is a vital requirement now-a-days. The use of real-time object detection system for the improvement of surveillance is a promising application of Convolutional Neural Networks (CNN). We are concerned about the real-time detection of weapons for the surveillance cameras, so we focused on the implementation and comparison of faster approaches such as Region (R-CNN) and Region Fully Convolutional Networks (R-FCN) with feature extractor Visual Geometry Group (VGG)… More >

Xi Chen^1,3, Tao Wu^2,*, Mehtab Afzal⁴

CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 1959-1971, 2022, DOI:10.32604/cmc.2022.022344

Abstract Animal husbandry is the pillar industry in some ethnic areas of China. However, the communication/networking infrastructure in these areas is often underdeveloped, thus the difficulty in centralized management, and challenges for the effective monitoring. Considering the dynamics of the field monitoring environment, as well as the diversity and mobility of monitoring targets, traditional WSN (Wireless Sensor Networks) or IoT (Internet of Things) is difficult to meet the surveillance needs. Mobile surveillance that features the collaboration of various functions (camera, sensing, image recognition, etc.) deployed on mobile devices is desirable in a volatile wireless environment. This paper proposes the service function… More >

R. Dhaya^1,*, R. Kanthavel²

Intelligent Automation & Soft Computing, Vol.32, No.1, pp. 183-192, 2022, DOI:10.32604/iasc.2022.021538

Abstract The high prevalence of urban flooding in the world is increasing rapidly with the rise in extreme weather events. Consequently, this research uses an Automatic Flood Monitoring System (ARMS) through a video surveillance camera. Initially, videos are collected from a surveillance camera and converted into video frames. After converting the video frames, the water level can be identified by using a Histogram of oriented Gradient (HoG), which is used to remove the functionality. Completing the extracted features, the frames are enhanced by using a median filter to remove the unwanted noise from the image. The next step is water level… More >

Mesfer Al Duhayyim¹, Haya Mesfer Alshahrani², Fahd N. Al-Wesabi³, Mohammed Alamgeer⁴, Anwer Mustafa Hilal^5,*, Manar Ahmed Hamza⁵

CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 5871-5887, 2022, DOI:10.32604/cmc.2022.021093

Abstract An exponential growth in advanced technologies has resulted in the exploration of Ocean spaces. It has paved the way for new opportunities that can address questions relevant to diversity, uniqueness, and difficulty of marine life. Underwater Wireless Sensor Networks (UWSNs) are widely used to leverage such opportunities while these networks include a set of vehicles and sensors to monitor the environmental conditions. In this scenario, it is fascinating to design an automated fish detection technique with the help of underwater videos and computer vision techniques so as to estimate and monitor fish biomass in water bodies. Several models have been… More >

Osama S. Faragallah^1,*, Sultan S. Alshamrani¹, Heba M. El-Hoseny², Mohammed A. AlZain¹, Emad Sami Jaha³, Hala S. El-Sayed⁴

Intelligent Automation & Soft Computing, Vol.31, No.3, pp. 1483-1497, 2022, DOI:10.32604/iasc.2022.020330

Abstract Crowd monitoring analysis has become an important challenge in academic researches ranging from surveillance equipment to people behavior using different algorithms. The crowd counting schemes can be typically processed in two steps, the images ground truth density maps which are obtained from ground truth density map creation and the deep learning to estimate density map from density map estimation. The pandemic of COVID-19 has changed our world in few months and has put the normal human life to a halt due to its rapid spread and high danger. Therefore, several precautions are taken into account during COVID-19 to slowdown the… More >

Zhen Xu¹, Xiaoqian Zeng¹, Genlin Ji^1,*, Bo Sheng²

Intelligent Automation & Soft Computing, Vol.31, No.3, pp. 1703-1717, 2022, DOI:10.32604/iasc.2022.016919

Abstract Anomaly detection in surveillance videos is an extremely challenging task due to the ambiguous definitions for abnormality. In a complex surveillance scenario, the kinds of abnormal events are numerous and might co-exist, including such as appearance and motion anomaly of objects, long-term abnormal activities, etc. Traditional video anomaly detection methods cannot detect all these kinds of abnormal events. Hence, we utilize multiple probabilistic models inference to detect as many different kinds of abnormal events as possible. To depict realistic events in a scene, the parameters of our methods are tailored to the characteristics of video sequences of practical surveillance scenarios.… More >

Abdul Hanan Ashraf¹, Muhammad Imran¹, Abdulrahman M. Qahtani^2,*, Abdulmajeed Alsufyani², Omar Almutiry³, Awais Mahmood³, Muhammad Attique⁴, Mohamed Habib^5,6

CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 2761-2775, 2022, DOI:10.32604/cmc.2022.018785

Abstract In recent years, the number of Gun-related incidents has crossed over 250,000 per year and over 85% of the existing 1 billion firearms are in civilian hands, manual monitoring has not proven effective in detecting firearms. which is why an automated weapon detection system is needed. Various automated convolutional neural networks (CNN) weapon detection systems have been proposed in the past to generate good results. However, These techniques have high computation overhead and are slow to provide real-time detection which is essential for the weapon detection system. These models have a high rate of false negatives because they often fail… More >

Altaf Hussain^1,2, Khan Muhammad¹, Hayat Ullah¹, Amin Ullah^1,4, Ali Shariq Imran³, Mi Young Lee¹, Seungmin Rho¹, Muhammad Sajjad^2,3,*

CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 2171-2190, 2022, DOI:10.32604/cmc.2022.018181

Abstract Digital surveillance systems are ubiquitous and continuously generate massive amounts of data, and manual monitoring is required in order to recognise human activities in public areas. Intelligent surveillance systems that can automatically ide.pngy normal and abnormal activities are highly desirable, as these would allow for efficient monitoring by selecting only those camera feeds in which abnormal activities are occurring. This paper proposes an energy-efficient camera prioritisation framework that intelligently adjusts the priority of cameras in a vast surveillance network using feedback from the activity recognition system. The proposed system addresses the limitations of existing manual monitoring surveillance systems using a… More >

Samia Riaz¹, Muhammad Waqas Anwar², Irfan Riaz³, Hyun-Woo Kim⁴, Yunyoung Nam^4,*, Muhammad Attique Khan⁵

CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 1-17, 2022, DOI:10.32604/cmc.2022.018268

Abstract The captured outdoor images and videos may appear blurred due to haze, fog, and bad weather conditions. Water droplets or dust particles in the atmosphere cause the light to scatter, resulting in very limited scene discernibility and deterioration in the quality of the image captured. Currently, image dehazing has gained much popularity because of its usability in a wide variety of applications. Various algorithms have been proposed to solve this ill-posed problem. These algorithms provide quite promising results in some cases, but they include undesirable artifacts and noise in haze patches in adverse cases. Some of these techniques take unrealistic… More >

Muhammad Ramzan^1,2,*, Adnan Abid¹, Shahid Mahmood Awan^1,3

CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 1829-1844, 2022, DOI:10.32604/cmc.2022.017522

Abstract In the current era, automatic surveillance has become an active research problem due to its vast real-world applications, particularly for maintaining law and order. A continuous manual monitoring of human activities is a tedious task. The use of cameras and automatic detection of unusual surveillance activity has been growing exponentially over the last few years. Various computer vision techniques have been applied for observation and surveillance of real-world activities. This research study focuses on detecting and recognizing unusual activities in an academic situation such as examination halls, which may help the invigilators observe and restrict the students from cheating or… More >