Altaf Hussain

• Doctor of Philosophy
• Research Assistant at Sejong University

Infrared (IR) technology has emerged as a solution for monitoring dark environments. It offers resilience to shifting illumination, appearance changes, and shadows, with applications spanning self-driving cars, robotics, nighttime security, and many other fields. While existing state-of-the-art RGB-based human action recognition (AR) models exhibit...

The Sports Action Recognition (SAR) domain is of significant importance in research, with diverse applications, ranging from aiding coaches in strategic decision-making to empowering athletes and contributing to real-time commercial entertainment. Despite the existence of extensive large-scale and small-scale datasets, the direct application of the...

Nowadays, surveillance systems play a pivotal role in monitoring various sectors to ensure public safety and security. These systems generate massive amounts of video data. Therefore, effective analysis of these streams is an important research area with multiple applications. Several methods have been reported for the automatic recognition of abno...

Nowadays, for controlling crime, surveillance cameras are typically installed in all public places to ensure urban safety and security. However, automating Human Activity Recognition (HAR) using computer vision techniques faces several challenges such as lowlighting, complex spatiotemporal features, clutter backgrounds, and inefficient utilization...

The growing demand for high-quality industrial products has led to a significant emphasis on image anomaly detection (AD). Anomaly detection in industrial goods presents a formidable research challenge that demands the application of sophisticated techniques to identify and address deviations from the expected norm accurately. Manufacturers increas...

The integration of artificial intelligence (AI) into human activity recognition (HAR) in smart surveillance systems has the potential to revolutionize behavior monitoring. These systems analyze an individual's physiological or behavioral features to continuously monitor and identify any unusual or suspicious activity in video streams, thereby impro...

In the field of research on wireless visual sensor networks, human activity recognition (HAR) using consumer electronics is now an emerging research area in both the academic and industrial sectors, with a diverse range of applications. However, the implementation of HAR through computer vision methods is highly challenging on consumer electronic d...

Deep-learning-based human activity recognition (HAR) methods have significantly transformed a wide range of domains over recent years. However, the adoption of Big Data techniques in industrial applications remains challenging due to issues such as generalized weight optimization, diverse viewpoints, and the complex spatiotemporal features of video...

Abstract Recent advancements in the Internet of Medical Things (IoMT) have revolutionized the healthcare sector, making it an active research area in the academic and industrial sectors. Following these advances, automatic Human Activity Recognition (HAR) is now integrated into the IoMT, facilitating remote patient monitoring systems for smart heal...

This survey explores electricity theft detection in smart grids, where traditional power systems meet modern technology. Smart grids, designed for efficient energy management and continuous integration of renewables, face a pressing challenge electricity theft, costing utility companies over $96 billion annually. The survey traces the evolution fro...

Rapid technological advancement and industrial growth pervade modern society. Light pollution has emerged as a significant environmental issue, resulting from excessive artificial nighttime lighting. It obscures celestial views and negatively impacts human health, contributing to sleep disorders and mood imbalances. This article introduces an immer...

A crucial component of designing intelligent and ecologically friendly environments nowadays is electricity consumption forecasting. The generation of energy can be enhanced to effectively meet the population's rising requirements by using the prediction of future electricity consumption. Due to the broad variety of consumption patterns, it is diff...

Human Activity Recognition (HAR) plays a crucial role in communication and the Internet of Things (IoT), by enabling vision sensors to understand and respond to human behavior more intelligently and efficiently. Existing deep learning models are complex to deal with the low illumination, diverse viewpoints, and cluttered backgrounds, which require...

The identification of anomalies in industrial settings poses a significant challenge, especially when there is a lack of negative samples and when the anomalous regions are small. Although existing computer vision methods have automated this task to some extent, these approaches struggle to extract salient features for inspecting defective chips. T...

Nowadays, renewable energy resources such as Photovoltaic (PV) is one of the convenient ways to integrate it into the distributed grid to fulfill the huge energy demands without burning costly and pollutant fossil fuels. Researchers have been contributing from various aspects to develop accurate PV-power forecasting methods however further improvem...

For efficient energy distribution, microgrids (MG) provide significant assistance to main grids and act as a bridge between the power generation and consumption. Renewable energy generation resources, particularly photovoltaics (PVs), are considered as a clean source of energy but are highly complex, volatile, and intermittent in nature making thei...

Human Activity Recognition is an active research area with several Convolutional Neural Network (CNN) based features extraction and classification methods employed for surveillance and other applications. However, accurate identification of HAR from a sequence of frames is a challenging task due to cluttered background, different viewpoints, low re...

Background and motivation: Every year, millions of Muslims worldwide come to Mecca to perform the Hajj. In order to maintain the security of the pilgrims, the Saudi government has installed about 5000 closed circuit television (CCTV) cameras to monitor crowd activity efficiently. Problem: As a result, these cameras generate an enormous amount of...

Recently, surveillance systems are globally installed for crime prevention by monitoring both private and public places which generate a massive amount of video data. This setup requires human experts to observe and monitor the ongoing activities continuously. To handle this tedious task, an automatic technique workable in real-time for violent act...

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 identify normal and abnormal activities are highly desirable, as these would allow for efficient monitoring b...

Chest x-rays acts as the most important component for diagnosis of chest related diseases. Millions of chest x-ray images are collected daily all over the world. Usually, a single radiologist is supposed to report a large amount of samples daily, which leads to problems like delay in time and inefficient diagnosis which sometimes pose serious threa...

Aerial images play a crucial role in many facets of worldly life. Areas such as automatic aerial disaster management or smart agriculture obviously require aerial imagery. In order to address these challenges properly, high-resolution images are fundamentally very desirable which is not ubiquitously available. For this purpose, we use modern image...

Emotion and gender recognition from facial features are important properties of human empathy. Robots should also have these capabilities. For this purpose, we present an approach to recognizing the gender and expression from real-time videos using convolution neural network (CNN). Both gender classification and emotion recognition in real environm...

The most important source of entertainment for children is cartoon, but it also introduces many ideas that are not favorable for them. To put element of fantasy and enchantment, violence is one of the unwanted feature that is present in some of the cartoons. The best approach to avoid children from such violent intense scenes in cartoon is to make...

In this era of automation, deep learning has a vital role in computer vision for objects detection. Deep learning provides powerful tools that are able to learn semantics, and high-level deep features to address the problems that exist in traditional architectures of hand-crafted feature extraction techniques like HOG and SIFT. In this paper we pro...