Modelling of Intelligent Object Detection and Classification using Aquila Optimizer with Deep Learning on Surveillance Videos

Object Detection (OD) in surveillance video is the way of automatically detecting and tracking object classes of interest within the video recording. It includes the application of a Computer Vision (CV) technique to analyze the video frame and identify the classes of objects or the presence of specific objects. Various OD techniques are used to find objects within the footage video. This algorithm analyzes the visual feature of the frames and employs Machine Learning (ML) approaches namely Deep Neural Network (DNN), to detect and track objects. It is worth mentioning that the accuracy and performance of OD in surveillance video depends on factors including the choice of algorithms and models, the availability of labelled training data, and the quality of the video frame for the specific object of interest. This study introduces a new modeling of Intelligent Object Recognition and Classification by employing Aquila Optimizer with Deep Learning (IODC-AODL) approach in Surveillance Video. The goal of the IODC-AODL technique is to integrate the DL model with the hyperparameter tuning process for object detection and classification. In the proposed IODC-AODL approach, a Faster RCNN method is enforced for the process of OD. Next, Long Short-Term Memory (LSTM) networking approach is implemented for the object classification process. At last, the AO approach is enforced for the optimum hyperparameter tuning of the LSTM network and it assists in improving the classifier rate. A widespread simulation sets are performed to exhibit the superior performance of the IODC-AODL approach. The experimental result analysis portrayed the supremacy of the IODC-AODL algorithm over other models

Enhancing Multi-Object Detection in Video Content: Exploring Hybrid Techniques and Method Combinations for Improved Classification Accuracy

The classification of objects within video content holds significant importance, particularly in the context of automated visual surveillance systems. Object classification refers to the procedure of categorizing objects into predefined and semantically meaningful groups based on their features. While humans find object classification in videos to be straightforward, machines face complexity and challenges in this task due to various factors like object size, occlusion, scaling, lighting conditions, and more. Consequently, the demand for analyzing video sequences has spurred the development of various techniques for object classification. This paper proposes hybrid techniques for multi object detection. The experimental analysis focused on a vehicles-openimages dataset containing 627 different catagories of vehicles. The results emphasize the profound impact of method combinations on image classification accuracy. Two primary methods, wavelet transformation and Principal Component Analysis (PCA), were employed alongside Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN). The evaluation encompassed performance metrics, including accuracy, precision, recall, specificity, and F1 score. In the analysis, Wavelet + RNN" combination consistently achieved the highest accuracy across all performance metrics, including accuracy percentage (96.76%), precision (96.76%), recall (86.32%), F1 score (87.12%), and specificity (87.43%). In addition, the hybrid classifiers were subjected for image classification of different vehicle catagories. In the analysis of different catagories, Wavelet + RNN" emerges as the standout performer, consistently achieving high accuracy percentages across all object categories, ranging from 82.87% for identifying People to 90.12% for recognizing Trucks

Covid-19 detection from chest x-ray images: comparison of well-established convolutional neural networks models

Coronavirus disease 19 (Covid-19) is a pandemic disease that has already killed hundred thousands of people and infected millions more. At the climax disease Covid-19, this virus will lead to pneumonia and result in a fatality in extreme cases. COVID-19 provides radiological cues that can be easily detected using chest X-rays, which distinguishes it from other types of pneumonic disease. Recently, there are several studies using the CNN model only focused on developing binary classifier that classify between Covid-19 and normal chest X-ray. However, no previous studies have ever made a comparison between the performances of some of the established pre-trained CNN models that involving multi-classes including Covid-19, Pneumonia and Normal chest X-ray. Therefore, this study focused on formulating an automated system to detect Covid-19 from chest X-Ray images by four established and powerful CNN models AlexNet, GoogleNet, ResNet-18 and SqueezeNet and the performance of each of the models were compared. A total of 21,252 chest X-ray images from various sources were pre-processed and trained for the transfer learning-based classification task, which included Covid-19, bacterial pneumonia, viral pneumonia, and normal chest x-ray images. In conclusion, this study revealed that all models successfully classify Covid-19 and other pneumonia at an accuracy of more than 78.5%, and the test results revealed that GoogleNet outperforms other models for achieved accuracy of 91.0%, precision of 85.6%, sensitivity of 85.3%, and F1 score of 85.4%

A Study on Object Detection and Tracking of a Mobile Robot Using CIE L^* a^* b^* Color Space

Autonomous vehicles are increasingly used in daily life and industrial applications. Mobile robot technologies lead to autonomous architectures in these areas. The path planning methods of mobile robots contain differences in the purpose they realize. This trajectory planning from a determined starting point to the target point brings many techniques from image processing to artificial intelligence. In the study, an application with a unique design has been carried out on the tracking of circular objects with different diameters and colors by a mobile robot. Moving object is detected with CIE L^* a^* b^* color space with RGB-D camera by utilizing the ROS server-client architecture. The mobile robot tracks the detected object at a certain distance at a constant speed. Image filtering parameters are processed by the mobile robot in the Matlab environment together with the publisher-subscriber parameters. Thus, two circular objects with different colors, detected because of image processing and determined beforehand, are continuously followed by the mobile robot at a certain speed. Experiments were carried out using different diameter, size tolerance and color parameters in the image depending on the CIE L^* a^* b^* color space

TBC-K-Means based Co-Located Object Recognition with Co-Located Object Status Identification Framework Using MAX-GRU

In the application of detached object recognition in public places like railway terminals, the recognition of the co-located objects in the video is a more vital process. Nevertheless, owing to the occurrence of multiple co-located object instances, the analysis of the status of the co-located object in the video is a challenging process. Hence, for solving this issue, this paper proposes the Min-Max Distance based K-Means (MMD-K-Means)-centric co-located object recognition with object status identification. Primarily, the input video from the railway is converted to frames. Subsequently, it was improved using Contrast Limited Adaptive Histogram Equalization (CLAHE). Next, Tukey’s Bi-weight Correlation-based Byte Tacking (TBC-BT) and MMD-K-Means clustering are done for the detection and tracking of moving and non-moving objects. Subsequently, the Cyclic Neighbor-based Connected Component Analysis (CN-CCA) process was done from the static and moving object-detected frames for the main and co-located object labeling. Next, it executed the patch extraction for the separate analysis of each instance. At last, the Maxout-based Gated Recurrent Unit (Max-GRU) determined the object status in CN-CCA processed frame with the estimated distance between objects and extracted features from the static objects. The proposed system was then experimentally examined and validated in contrast to the standard methods. The proposed MMD-K-Means achieved a co-located object identification rate of 97.92% in 1184 milliseconds. Next, the Max-GRU achieved 98.13% identification accuracy, and it also achieved excellent results for other performance parameters. The proposed system’s performance is experimentally proved with several performance metrics

Rare And Popular Event-Based Co-Located Pattern Recognition in Surveillance Videos Using Max-Min PPI-DBSCAN And GREVNN

Co-located pattern recognition is the process of identifying the sequence of patterns occurring in surveillance videos. In greater part of the existing works, the detection of rare and popular events for effective co-located pattern recognition is not concentrated. Therefore, this paper presents the automatic discovery of the co-located patterns based on rare and popular events in the video. First, the video is converted to frames, and the keyframes are preprocessed. Then, the foreground and background of the frames are estimated, and the rare and popular events are grouped using Maximum-Minimum Pixel-Per-Inch Density-Based Spatial Clustering of Applications with Noise (Max-MinPPI-DBSCAN). From the grouped image, the object detection and mapping are done, and the patch is extracted from it. Next, the edges are detected and from that, for the moving objects, motion is estimated by the Kullback-Leibler Kalman Filter (KLKF). Also, for non-moving objects, the objects/persons are tracked. From the motion estimated and tracked data, time series features are extracted. Then, the optimal features are selected using the Dung Beetle State Transition Probability Optimizer (DBSTPO). Finally, the co-located pattern is classified using a Generalized Recurrent Extreme Value Neural Network (GREVNN), and the alert message is given to the authorities. Hence, the proposed model selected the features in 53239.44ms and classified the event with 99.0723% accuracy and showed better performance than existing works

Advancements In Crowd-Monitoring System: A Comprehensive Analysis of Systematic Approaches and Automation Algorithms: State-of-The-Art

Growing apprehensions surrounding public safety have captured the attention of numerous governments and security agencies across the globe. These entities are increasingly acknowledging the imperative need for reliable and secure crowd-monitoring systems to address these concerns. Effectively managing human gatherings necessitates proactive measures to prevent unforeseen events or complications, ensuring a safe and well-coordinated environment. The scarcity of research focusing on crowd monitoring systems and their security implications has given rise to a burgeoning area of investigation, exploring potential approaches to safeguard human congregations effectively. Crowd monitoring systems depend on a bifurcated approach, encompassing vision-based and non-vision-based technologies. An in-depth analysis of these two methodologies will be conducted in this research. The efficacy of these approaches is contingent upon the specific environment and temporal context in which they are deployed, as they each offer distinct advantages. This paper endeavors to present an in-depth analysis of the recent incorporation of artificial intelligence (AI) algorithms and models into automated systems, emphasizing their contemporary applications and effectiveness in various contexts