Normalized Attention Neural Network with Adaptive Feature Recalibration for Detecting the Unusual Activities Using Video Surveillance Camera

Abstract

Over the past few years, surveillance cameras have become common in many homes and businesses. Many businesses still employ a human monitor of their cameras, despite the fact that this individual is more probable to miss some anomalous occurrences in the video feeds owing to the inherent limitations of human perception. Numerous scholars have investigated surveillance data and offered several strategies for automatically identifying anomalous occurrences. Therefore, it is important to build a model for identifying unusual occurrences in the live stream from the security cameras. Recognizing potentially dangerous situations automatically so that appropriate action may be taken is crucial and can be of great assistance to law enforcement. In this research work, starting with an MRCNN for feature extraction and AFR for fine-tuning, this architecture has a number of key components (AFR). To increase the quality of the features extracted by the MRCNN, the AFR replicas the inter-dependencies among the features to enhance the quality of the low-and high-frequency features extracted. Then, a normalized attention network (NAN) is used to learn the relationships between channels, which used to identify the violence and speeds up the convergence process for training a perfect. Furthermore, the dataset took real-time security camera feeds from a variety of subjects and situations, as opposed to the hand-crafted datasets utilized in prior efforts. We also demonstrate the method's capability of assigning the correct category to each anomaly by classifying normal and abnormal occurrences. The method divided the information gathered into three primary groups: those in need of fire protection, those experiencing theft or violence, and everyone else. The study applied the proposed approach to the UCF-Crime dataset, where it outperformed other models on the same dataset.

A Deep Reinforcement Learning-Based RNN Model in a Traffic Control System for 5G-Envisioned Internet of Vehicles

“…At this time, the input from the (𝑤 − 1) 𝑖 layer to the wth layer is represented as 𝑤 (𝑤) ∈ 𝐿 𝑓×𝑈 , and the recurring weight of the wth layer is characterized as 𝑤 (𝑤) ∈ 𝐿 𝑓×𝑓 . Here, however, the mechanisms of the input vector are uttered as, 𝐴 𝑖 (𝑤,𝑥) = ∑ 𝑝 𝑎𝑚 (𝑤) 𝑂 𝑚 (𝑤−1,𝑥) + ∑ 𝑜 𝑎𝑎 (𝑤) 𝑂 𝑎 (𝑤,𝑥−1) 𝑓 𝑎 𝑈 𝑘=1 (3) where, 𝑝 𝑎𝑚 (𝑤) and 𝑜 𝑎𝑎 (𝑤) are the elements of 𝑤 (𝑤) and 𝜔 𝜔 a signifies the arbitrary unit quantity of the w th layer. The rudiments of the output vector of the w ith layer are signified as, 𝑂 𝑎 (𝑤,𝑥) = 𝛾 ((𝜔)) (𝐹 𝑎 (𝑤,𝑥) )…”

“…ITS, which dynamically arranges traffic signals to dismiss traffic mobbing and improve driving knowledge, has been focusing on IoCV traffic control, among other applications [3]. Growth in the global population and the subsequent proliferation of automobiles contribute to urban congestion.…”

Detecting False Data Injection Attacks in Industrial Internet of Things Using an Optimized Bidirectional Gated Recurrent Unit-Swarm Optimization Algorithm Model

“…IIoT services collect the huge amounts of information they have produced from a wide range of sources, and then share the information and make it easy to be accessed safely [7]. Thus, the industrial applicability and reliability expansion of the IIoT depends critically on the security of its data dissemination service [8]. These applications rely on data to make decisions.…”