RETRACTED ARTICLE: Novel deep learning framework for broadcasting abnormal events obtained from surveillance applications

Krishnaveni, P.; Sutha, J.

doi:10.1007/s12652-019-01668-6

Cited by 14 publications

(4 citation statements)

References 26 publications

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“…LD devices may not be as successful due to a number of issues, such as road obstructions, ambient lighting sources, and noisy weather. To address these issues, a shadow-resistant methodology that makes use of Fourier transformation and the maximally stable extreme region (MSER) approach in the blue color stream is used as a reference [167]. The research findings [168] suggest that the minimal safe separation between the self-absorbed car and the vehicle should be used to estimate the extraction of ROI.…”

Section: Approaches For Pedestrian Detectionmentioning

confidence: 99%

Object Detection in Autonomous Vehicles under Adverse Weather: A Review of Traditional and Deep Learning Approaches

Tahir,

Zhang,

Asim

et al. 2024

Algorithms

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Enhancing the environmental perception of autonomous vehicles (AVs) in intelligent transportation systems requires computer vision technology to be effective in detecting objects and obstacles, particularly in adverse weather conditions. Adverse weather circumstances present serious difficulties for object-detecting systems, which are essential to contemporary safety procedures, infrastructure for monitoring, and intelligent transportation. AVs primarily depend on image processing algorithms that utilize a wide range of onboard visual sensors for guidance and decisionmaking. Ensuring the consistent identification of critical elements such as vehicles, pedestrians, and road lanes, even in adverse weather, is a paramount objective. This paper not only provides a comprehensive review of the literature on object detection (OD) under adverse weather conditions but also delves into the ever-evolving realm of the architecture of AVs, challenges for automated vehicles in adverse weather, the basic structure of OD, and explores the landscape of traditional and deep learning (DL) approaches for OD within the realm of AVs. These approaches are essential for advancing the capabilities of AVs in recognizing and responding to objects in their surroundings. This paper further investigates previous research that has employed both traditional and DL methodologies for the detection of vehicles, pedestrians, and road lanes, effectively linking these approaches with the evolving field of AVs. Moreover, this paper offers an in-depth analysis of the datasets commonly employed in AV research, with a specific focus on the detection of key elements in various environmental conditions, and then summarizes the evaluation matrix. We expect that this review paper will help scholars to gain a better understanding of this area of research.

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Section: Approaches For Pedestrian Detectionmentioning

confidence: 99%

Object Detection in Autonomous Vehicles under Adverse Weather: A Review of Traditional and Deep Learning Approaches

Tahir,

Zhang,

Asim

et al. 2024

Algorithms

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“…Military research organizations have increased the research funding to drive the adoption of ML/DL-driven applications for military applications. Recent research contributions related to the use of DL techniques in defense and military applications can be found in [136][137][138][139][140][141]. A comparison of prominent studies is presented in Table 8.…”

Section: Defensementioning

confidence: 99%

Deep Learning for the Industrial Internet of Things (IIoT): A Comprehensive Survey of Techniques, Implementation Frameworks, Potential Applications, and Future Directions

Latif

Driss

Boulila

et al. 2021

Sensors

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The Industrial Internet of Things (IIoT) refers to the use of smart sensors, actuators, fast communication protocols, and efficient cybersecurity mechanisms to improve industrial processes and applications. In large industrial networks, smart devices generate large amounts of data, and thus IIoT frameworks require intelligent, robust techniques for big data analysis. Artificial intelligence (AI) and deep learning (DL) techniques produce promising results in IIoT networks due to their intelligent learning and processing capabilities. This survey article assesses the potential of DL in IIoT applications and presents a brief architecture of IIoT with key enabling technologies. Several well-known DL algorithms are then discussed along with their theoretical backgrounds and several software and hardware frameworks for DL implementations. Potential deployments of DL techniques in IIoT applications are briefly discussed. Finally, this survey highlights significant challenges and future directions for future research endeavors.

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“…Convolutional Neural Networks (CNN) and Deep Neural Networks (DNN), two types of existing artificial intelligence (AI) and deep learning (DL) methods in field of context‐aware surveillance systems, are primarily used for static image analysis instead of image broadcasting or video analysis [11]. Several current video surveillance systems depend on conventional centralised or cloud‐based solutions, which have large data communication overhead, large speed, and enormous packet loss limitations, with a focus on distributed video surveillance systems and AI algorithms [12]. For dispersed computing clusters and cloud computing platforms, number of distributed AI and DL methods, including distributed CNN, DNN, Deep Belief Network, GoogleNet, MobileNet, ResNet101, and Long short‐term memory, have been proposed in prior research [13].…”

Section: Introductionmentioning

confidence: 99%

Abnormal event detection model using an improved ResNet101 in context aware surveillance system

Kalshetty¹,

Parveen²

2023

Cognitive Comp and Systems

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Surveillance system plays a significant role for achieving security monitoring in the place of crowd areas. Offline monitoring of these crowd activity is quite challenging because it requires huge number of human resources for attaining efficient tracking. For shortcoming these issue automated and intelligent based system must be developed for efficiently monitor crowd and detect abnormal activity. However the existing methods faces issues like irrelevant features, high cost and process complexity. In this current research context aware surveillance‐system utilising hybrid ResNet101‐ANN is developed for effective abnormal activity detection. For this proposed approach video acquired from surveillance camera is considered as input. Then, acquired video is segmented into multiple frames. After that pre‐processing techniques such as denoising using mean filter, motion deblurring, contrast enhancement using Histogram Equalisation and canny edge detection is applied in this segmented frames. Further, the pre‐processed frame is fetched into hybrid ResNet101‐ANN classifier for abnormal event classification. Here, ResNet101 is used for extracting the features from the frames and Artificial neural network which replaces the fully connected layer of ResNet101 us used to detect the abnormal activity. If once abnormal‐events detected the context aware services generate alert to the user for preventing abnormal‐activities. Accuracy, precision, recall, and error values reached for the proposed‐model on simulation were 0.98, 0.98, 0.98 and 0.017 respectively. Using this proposed model effective crowd monitoring and abnormal activity detection can be achieved.

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RETRACTED ARTICLE: Novel deep learning framework for broadcasting abnormal events obtained from surveillance applications

Cited by 14 publications

References 26 publications

Object Detection in Autonomous Vehicles under Adverse Weather: A Review of Traditional and Deep Learning Approaches

Object Detection in Autonomous Vehicles under Adverse Weather: A Review of Traditional and Deep Learning Approaches

Deep Learning for the Industrial Internet of Things (IIoT): A Comprehensive Survey of Techniques, Implementation Frameworks, Potential Applications, and Future Directions

Abnormal event detection model using an improved ResNet101 in context aware surveillance system

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