Browsing by Author "Ullah, Amin"
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Item Activity Recognition Using Temporal Optical Flow Convolutional Features and Multilayer LSTM(2019-12) Ullah, Amin; Muhammad, Khan; Del Ser, Javier; Baik, Sung Wook; De Albuquerque, Victor Hugo C.; IANowadays digital surveillance systems are universally installed for continuously collecting enormous amounts of data, thereby requiring human monitoring for the identification of different activities and events. Smarter surveillance is the need of this era through which normal and abnormal activities can be automatically identified using artificial intelligence and computer vision technology. In this paper, we propose a framework for activity recognition in surveillance videos captured over industrial systems. The continuous surveillance video stream is first divided into important shots, where shots are selected using the proposed convolutional neural network (CNN) based human saliency features. Next, temporal features of an activity in the sequence of frames are extracted by utilizing the convolutional layers of a FlowNet2 CNN model. Finally, a multilayer long short-term memory is presented for learning long-term sequences in the temporal optical flow features for activity recognition. Experiments11https://github.com/Aminullah6264/Activity-Rec-ML-LSTM. are conducted using different benchmark action and activity recognition datasets, and the results reveal the effectiveness of the proposed method for activity recognition in industrial settings compared with state-of-the-art methods.Item Artificial Intelligence of Things-assisted two-stream neural network for anomaly detection in surveillance Big Video Data(2022-04) Ullah, Waseem; Ullah, Amin; Hussain, Tanveer; Muhammad, Khan; Heidari, Ali Asghar; Del Ser, Javier; Baik, Sung Wook; De Albuquerque, Victor Hugo C.; IAIn the last few years, visual sensors are deployed almost everywhere, generating a massive amount of surveillance video data in smart cities that can be inspected intelligently to recognize anomalous events. In this work, we present an efficient and robust framework to recognize anomalies from surveillance Big Video Data (BVD) using Artificial Intelligence of Things (AIoT). Smart surveillance is an important application of AIoT and we propose a two-stream neural network in this direction. The first stream comprises instant anomaly detection that is functional over resource-constrained IoT devices, whereas second phase is a two-stream deep neural network allowing for detailed anomaly analysis, suited to be deployed as a cloud computing service. Firstly, a self-pruned fine-tuned lightweight convolutional neural network (CNN) classifies the ongoing events as normal or anomalous in an AIoT environment. Upon anomaly detection, the edge device alerts the concerned departments and transmits the anomalous frames to cloud analysis center for their detailed evaluation in the second phase. The cloud analysis center resorts to the proposed two-stream network, modeled from the integration of spatiotemporal and optical flow features through the sequential frames. Fused features flow through a bi-directional long short-term memory (BD-LSTM) layer, which classifies them into their respective anomaly classes, e.g., assault and abuse. We perform extensive experiments over benchmarks built on top of the UCF-Crime and RWF-2000 datasets to test the effectiveness of our framework. We report a 9.88% and 4.01% increase in accuracy when compared to state-of-the-art methods evaluated over the aforementioned datasets.Item Deep Learning for Safe Autonomous Driving: Current Challenges and Future Directions(2021-07) Muhammad, Khan; Ullah, Amin; Lloret, Jaime; Ser, Javier Del; De Albuquerque, Victor Hugo C.; IAAdvances in information and signal processing technologies have a significant impact on autonomous driving (AD), improving driving safety while minimizing the efforts of human drivers with the help of advanced artificial intelligence (AI) techniques. Recently, deep learning (DL) approaches have solved several real-world problems of complex nature. However, their strengths in terms of control processes for AD have not been deeply investigated and highlighted yet. This survey highlights the power of DL architectures in terms of reliability and efficient real-time performance and overviews state-of-the-art strategies for safe AD, with their major achievements and limitations. Furthermore, it covers major embodiments of DL along the AD pipeline including measurement, analysis, and execution, with a focus on road, lane, vehicle, pedestrian, drowsiness detection, collision avoidance, and traffic sign detection through sensing and vision-based DL methods. In addition, we discuss on the performance of several reviewed methods by using different evaluation metrics, with critics on their pros and cons. Finally, this survey highlights the current issues of safe DL-based AD with a prospect of recommendations for future research, rounding up a reference material for newcomers and researchers willing to join this vibrant area of Intelligent Transportation Systems.Item Modelling Electricity Consumption During the COVID19 Pandemic: Datasets, Models, Results and a Research Agenda(2023-09-01) Khan, Zulfiqar Ahmad; Hussain, Tanveer; Ullah, Amin; Ullah, Waseem; Del Ser, Javier; Muhammad, Khan; Sajjad, Muhammad; Baik, Sung Wook; IAThe COVID19 pandemic has impacted the global economy, social activities, and Electricity Consumption (EC), affecting the performance of historical data-based Electricity Load Forecasting (ELF) algorithms. This study thoroughly analyses the pandemic's impact on these models and develop a hybrid model with better prediction accuracy using COVID19 data. Existing datasets are reviewed, and their limited generalization potential for the COVID19 period is highlighted. A dataset of 96 residential customers, comprising 36 and six months before and after the pandemic, is collected, posing significant challenges for current models. The proposed model employs convolutional layers for feature extraction, gated recurrent nets for temporal feature learning, and a self-attention module for feature selection, leading to better generalization for predicting EC patterns. Our proposed model outperforms existing models, as demonstrated by a detailed ablation study using our dataset. For instance, it achieves an average reduction of 0.56% & 3.46% in MSE, 1.5% & 5.07% in RMSE, and 11.81% & 13.19% in MAPE over the pre- and post-pandemic data, respectively. However, further research is required to address the varied nature of the data. These findings have significant implications for improving ELF algorithms during pandemics and other significant events that disrupt historical data patterns.Item Multiview Summarization and Activity Recognition Meet Edge Computing in IoT Environments(2021-06-15) Hussain, Tanveer; Muhammad, Khan; Ullah, Amin; Ser, Javier Del; Gandomi, Amir H.; Sajjad, Muhammad; Baik, Sung Wook; De Albuquerque, Victor Hugo C.; IAMultiview video summarization (MVS) has not received much attention from the research community due to inter-view correlations and views' overlapping, etc. The majority of previous MVS works are offline, relying on only summary, and require additional communication bandwidth and transmission time, with no focus on foggy environments. We propose an edge intelligence-based MVS and activity recognition framework that combines artificial intelligence with Internet of Things (IoT) devices. In our framework, resource-constrained devices with cameras use a lightweight CNN-based object detection model to segment multiview videos into shots, followed by mutual information computation that helps in a summary generation. Our system does not rely solely on a summary, but encodes and transmits it to a master device using a neural computing stick for inter-view correlations computation and efficient activity recognition, an approach which saves computation resources, communication bandwidth, and transmission time. Experiments show an increase of 0.4 unit in F -measure on an MVS Office dateset and 0.2% and 2% improved accuracy for UCF-50 and YouTube 11 datesets, respectively, with lower storage and transmission times. The processing time is reduced from 1.23 to 0.45 s for a single frame and optimally 0.75 seconds faster MVS. A new dateset is constructed by synthetically adding fog to an MVS dateset to show the adaptability of our system for both certain and uncertain IoT surveillance environments.