Home security and surveillance systems are rapidly evolving, with Artificial Intelligence (AI) playing a transformative role in enhancing safety and threat detection. While several AI methods and datasets for intruder-related risk assessment exist, they predominantly focus on face detection and recognition, leaving a significant gap in addressing high-risk scenarios involving malicious intent, such as theft or harm. The lack of dedicated datasets for recognizing complex intruder activities, such as carrying weapons or engaging in destructive actions like kicking doors or breaking locks, limits the development of robust solutions. This work bridges this gap by introducing the Intruder Activity Recognition Dataset (IARD), a video dataset specifically designed to recognize four critical intruder activities: Armed Intruder, Door Kick, Intruder Inside and Lock Breaking. Leveraging IARD, we thoroughly benchmark various state-of-the-art methods, among which a Vision Transformer is found to achieve an impressive 93.3% accuracy in recognizing intruder actions. Our contribution highlights the potential of IARD in advancing AI-driven surveillance systems, providing a foundational dataset and benchmark for recognizing complex intruder activities.

Digital publishing’s exponential growth has created vast scholarly collections. Guiding researchers to relevant resources is crucial, and knowledge graphs (KGs) are key tools for unlocking hidden knowledge. However, current methods focus on external links between concepts, ignoring the rich information within individual papers. Challenges like insufficient multi-relational data, name ambiguity, and cold-start issues further limit existing KG-based methods, failing to capture the intricate attributes of diverse entities. To solve these issues, we propose GLAMOR, a robust KG framework encompassing entities e.g., authors, papers, fields of study, and concepts, along with their semantic interconnections. GLAMOR uses a novel random walk-based KG text generation method and then fine-tunes the language model using the generated text. Subsequently, the acquired context-preserving embeddings facilitate superior top@k predictions. Evaluation results on two public benchmark datasets demonstrate our GLAMOR’s superiority against state-of-the-art methods especially in solving the cold-start problem.

The main challenge faced by video‐based real‐world anomaly detection systems is the accurate learning of unusual events that are irregular, complicated, diverse, and heterogeneous in nature. Several techniques utilizing deep learning have been created to detect anomalies, yet their effectiveness on real‐world data is often limited due to the insufficient incorporation of motion patterns. To address these problems and enhance the traditional functionality of anomaly detection systems for surveillance video data, we propose a weakly supervised graph neural‐network‐assisted video anomaly detection framework called AD‐Graph. To identify temporal information from a series of frames, we extract 3D visual and motion features and represent these in a language‐based knowledge graph format. Next, a robust clustering strategy is applied to group together meaningful neighbourhoods of the graph with similar vertices. Furthermore, spectral filters are applied to these graphs, and spectral graph theory is used to generate graph signals and detect anomalous events. Extensive experimental results over two challenging datasets, UCF‐Crime and ShanghaiTech, show improvements of 0.35% and 0.78% against a state‐of‐the‐art model.

No system entity within a contemporary distributed cyber system can be entirely trusted. Hence, the classic centralized trust management method cannot be directly applied to it. Blockchain technology is essential to achieving decentralized trust management, its consensus mechanism is useful in addressing large-scale data sharing and data consensus challenges. Herein, an n-party quantum detectable Byzantine agreement (DBA) based on the GHZ state to realize the data consensus in a quantum blockchain is proposed, considering the threat posed by the growth of quantum information technology on the traditional blockchain. Relying on the nonlocality of the GHZ state, the proposed protocol detects the honesty of nodes by allocating the entanglement resources between different nodes. The GHZ state is notably simpler to prepare than other multi-particle entangled states, thus reducing preparation consumption and increasing practicality. When the number of network nodes increases, the proposed protocol provides better scalability and stronger practicability than the current quantum DBA. In addition, the proposed protocol has the optimal fault-tolerant found and does not rely on any other presumptions. A consensus can be reached even when there are n−2 traitors. The performance analysis confirms viability and effectiveness through exemplification. The security analysis also demonstrates that the quantum DBA protocol is unconditionally secure, effectively ensuring the security of data and realizing data consistency in the quantum blockchain.

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.

Video surveillance has shown encouraging outcomes to monitor human activities and prevent crimes in real time. To this extent, violence detection (VD) has received substantial attention from the research community due to its vast applications, such as ensuring security over public areas and industrial settings through smart machine intelligence. However, because of changing illumination, complex background and low resolution, the analysis of violence patterns remains challenging in the industrial video surveillance domain. In this paper, we propose a computationally intelligent VD approach to precisely detect violent scenes through deep analysis of surveillance video sequential patterns. First, the video stream acquired through the vision sensor is processed by a lightweight convolutional neural network (CNN) for the segmentation of important shots. Next, temporal optical flow features are extracted from the informative shots via a residential optical flow CNN. These are concatenated with appearance-invariant features extracted from a Darknet CNN model. Finally, a multilayer long short-term memory network is plugged to generate the final feature map for learning the violence patterns in a sequence of frames. In addition, we contribute to the existing surveillance VD data set by considering its indoor and outdoor scenarios separately for the proposed method's evaluation, achieving a 2% increase in accuracy over surveillance fight data set. Experiments also show encouraging results over the state of the art on other challenging benchmark data sets.