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 limitations in scalability for action understanding under uncertain, low-light, or dark conditions. Integrating these with IR data faces challenges due to changes in modality, high resource demands, and strict latency requirements. Such issues hinder the deployment of these technologies in real-world settings. To overcome these challenges, we introduce a novel slow-fast tubelet (SFT) processing framework designed for efficient and accurate AR in IR-based scenarios. The SFT framework comprises three modules: tubelet preprocessing (TPP), feature extraction, and the feature lateral connection and recognition module (FELCM). The TPP module refines IR streams by extracting the region of interest, filtering detected objects, removing noise, and generating tubelets of refined frames. The FELCM processes refined tubelet through two pathways, where the fast tubelet path operates at a high rate and the slow tubelet path operates at a slow rate. These pathways interconnect through lateral connections, facilitating mutual updates, and enhancing the prediction efficiency. We conducted extensive experiments on benchmark datasets, including NTURGB-D 120 and infrared action recognition (InfAR). The results demonstrate that our proposed SFT framework surpasses state-of-the-art approaches in terms of accuracy (2.7% and 3.3% improvement, respectively), computational cost, and inference latency while maintaining the competitive recognition performance. Our framework's promising results underscore its potential for direct deployment in real-world applications.

The main objective of this study is to propose a cyber–physical system (CPS)-based person reidentification (P-ReID) framework for smart surveillance. The Internet of Things (IoT)-based interconnected vision sensors in smart cities are considered essential elements of a CPS, and contribute significantly to urban security. However, the reidentification of targeted persons using emerging edge AI techniques still faces certain challenges. To improve efficiency at the edge and overcome the traditional sensing of video cameras, we employed an AI-based P-ReID framework for CPS that is functional in IoT environments. In addition, we present dual attention dilated network (DADNet), which integrates an energy-efficient convolutional neural network (CNN) with a self-attention module to substantially improve the person matching probability. Furthermore, we applied dual feature fusion to intelligently integrate discriminative and robust features using early and late fusion strategies that allow DADNet to significantly consider the foreground and marginally utilize the background information. Furthermore, we impose diversity orthogonality regularization over several CNN layers, which boosts the performance of DADNet, resulting in an appropriate usage over IoT networks. A comprehensive set of ablation studies, comparison with other state-of-the-art approaches, and a time complexity analysis confirm the strength of our DADNet for reidentification tasks in AI-enabled IoT settings that are well suited for a CPS.

Power generation (PG) prediction from renewable energy sources (RESs) plays a vital role in effective energy management in smart cities. However, harnessing the potential of edge intelligence in well-controlled Internet of Things (IoT) networks poses significant challenges. To address this, we propose an IoT-based framework for intelligent and efficient PG prediction in smart microgrids. The framework begins by acquiring data from various RESs, including wind and solar. Before the training process, the data undergoes cleaning and normalization steps that use denoising and cleansing filters. For forecasting renewable energy (RE), we introduce a hybrid model that integrates a multi-head attention (MHA)-based deep autoencoder (AE) with extreme gradient boosting (XGB) algorithm. The AE’s encoder component extracts discriminative features from the cleaned data sequence, which are then learned by XGB to provide a final PG forecast. This edge computing layer facilitates information sharing through fog computing, which ensures power balancing between suppliers and consumers. Furthermore, the framework also incorporates various power consumption (PC) sectors and entities within smart cities, such as transportation and healthcare, to ensure efficient management. We evaluate the proposed hybrid model using publicly accessible benchmarks and locally gathered data sets, demonstrating state-of-the-art performance in terms of error metrics. The computational complexity of the proposed model is also suitable for resource-constrained IoT devices connected to a shared IoT-Fog setup, enabling seamless communication with smart microgrids for effective power management.

Person reidentification (P-Reid) is an emerging research domain in the field of information retrieval that has gained exponential growth due to its wide range of applications in pedestrian tracking and crime prevention. The primary goal of P-Reid is to recognize a person based on previous appearance in multiview surveillance videos. The mainstream approaches apply fully supervised learning techniques that have poor scalability when deployed in complex real-world scenes, due to the overfitting problem, caused by the lack of sufficient annotated data. Further, optimization of these models for unlabeled data in real-time surveillance is a challenging task. To tackle these issues, an intelligent framework (LR-Net) is proposed, consisting of three tiers including fine-tuning (FT), siamese network (SN), and fusion strategy (FS). In the first tier, a deep learning model is fine-tuned for P-Reid that can handle both labeled and unlabeled data. Next, with the assistance of transfer learning, an SN is proposed that has a strong discriminative capability in terms of similarity between a pair of images. Finally, a learning-to-rank strategy is applied to optimize the learning capability of the SN, in which a triplet network extracts spatial-temporal patterns from unlabeled samples. In addition, a bayesian fusion model (BFM) is introduced to integrate the spatiotemporal and visual features, which yields 4.4%, 9.3%, and 0.8% improvement in the matching score over Market-1501, DukeMCMT-reID, and CUHK03 data sets, respectively. The conducted experiments and ablation study on the benchmark data sets empirically validate the proposed system, which obtains a high Rank-1 score as compared with the state-of-the-art (SOTA) methods.

Analyzing surveillance videos is mandatory for the public and industrial security. Overwhelming growth in computer vision fields has been made to automate the surveillance system in terms of human activity recognition, such as behavior analysis and violence detection (VD). However, it is challenging to detect and analyze the violent scenes intelligently to fulfill the notion of Industrial Internet of Things (IIoT)-based surveillance buoyed by constrained resources to reduce computational power. To tackle this challenge, in this article, an artificial intelligence enabled IIoT-based framework with VD-Network (VD-Net) is proposed. First, the input video frames are passed to light-weight convolutional neural network model for important information collection including humans or suspicious objects such as knives/guns. Upon suspicious object detection, an alert is generated as an earlier VD in IIoT network while the information is shared with concern departments. Only the frames with objects are forwarded to cloud for detail investigation where features are extracted using convolutional long short-term memory (ConvLSTM). The latter from ConvLSTM is propagated to gated recurrent unit for final VD. The conducted experiments and ablation study on the existing surveillance and nonsurveillance datasets empirically validate the effectiveness of the proposed VD-Net by improving 3.9% increase in the accuracy compared with the state-of-the-art VD methods.