End-to-end quantum-inspired method for vehicle classification based on video stream

2022 ◽  
Author(s):  
Hatim Derrouz ◽  
Alberto Cabri ◽  
Hamd Ait Abdelali ◽  
Rachid Oulad Haj Thami ◽  
François Bourzeix ◽  
...  
Keyword(s):  
Video Stream ◽  
Vehicle Classification ◽  
End To End

End-to-End View-Aware Vehicle Classification via Progressive CNN Learning

2017 ◽  
pp. 729-737
Author(s):  
Jiawei Cao ◽  
Wenzhong Wang ◽  
Xiao Wang ◽  
Chenglong Li ◽  
Jin Tang
Keyword(s):  
Vehicle Classification ◽  
End To End

scholarly journals An end-to-end covert channel via packet dropout for mobile networks

2018 ◽  
Vol 14 (5) ◽  
pp. 155014771877956 ◽  
Author(s):  
Yu-an Tan ◽  
Xinting Xu ◽  
Chen Liang ◽  
Xiaosong Zhang ◽  
Quanxin Zhang ◽  
...  
Keyword(s):  
Mobile Networks ◽  
Video Stream ◽  
Long Term Evolution ◽  
Packet Dropout ◽  
Covert Channel ◽  
Timing Channels ◽  
Term Evolution ◽  
Packet Delays ◽  
End To End

Voice over Long-Term Evolution enables reliable transmission among enormous Internet of Things devices, by providing end-to-end quality of service for Internet protocol–based services such as audio, video, and multimedia messaging. The research of covert timing channels aims at transmitting covert message stealthily to the receiver using variations of timing behavior. Existing approaches mainly modulate the covert message into inter-packet delays of overt traffic, which are not suitable for Voice over Long-Term Evolution, since most of the inter-packet delays of Voice over Long-Term Evolution traffic are of regular distribution, and any modification on inter-packet delays is easy to be detected. To address the issue, in this work, we propose a novel covert timing channel for the video stream in Voice over Long-Term Evolution, which modulates the covert message by deliberately dropping out video packets. Based on the two-dimensional mapping matrix, the blocks of covert message are mapped into dropout-packet sequence numbers. To recover the covert message, the receiver retrieves the sequence numbers of lost packets and identifies them to be translated into blocks of the covert message. To evaluate our scheme, the simulations with different packet loss rates are conducted to validate the undetectability, throughput, and robustness, finally, the results show that this scheme is effective and reliable.

scholarly journals End-to-End Deep One-Class Learning for Anomaly Detection in UAV Video Stream

2021 ◽  
Vol 7 (5) ◽  
pp. 90
Author(s):  
Slim Hamdi ◽  
Samir Bouindour ◽  
Hichem Snoussi ◽  
Tian Wang ◽  
Mohamed Abid
Keyword(s):  
Anomaly Detection ◽  
Optical Flow ◽  
Video Processing ◽  
Background Subtraction ◽  
Video Stream ◽  
Learning Method ◽  
End To End ◽  
Spatio Temporal ◽  
Uav Images

In recent years, the use of drones for surveillance tasks has been on the rise worldwide. However, in the context of anomaly detection, only normal events are available for the learning process. Therefore, the implementation of a generative learning method in an unsupervised mode to solve this problem becomes fundamental. In this context, we propose a new end-to-end architecture capable of generating optical flow images from original UAV images and extracting compact spatio-temporal characteristics for anomaly detection purposes. It is designed with a custom loss function as a sum of three terms, the reconstruction loss (Rl), the generation loss (Gl) and the compactness loss (Cl) to ensure an efficient classification of the “deep-one” class. In addition, we propose to minimize the effect of UAV motion in video processing by applying background subtraction on optical flow images. We tested our method on very complex datasets called the mini-drone video dataset, and obtained results surpassing existing techniques’ performances with an AUC of 85.3.

scholarly journals Gradient characteristics of a detection field for image data

2018 ◽  
Vol 19 (12) ◽  
pp. 741-744
Author(s):  
Zbigniew Czapla
Keyword(s):  
Image Data ◽  
Vehicle Detection ◽  
Image Sequence ◽  
Video Stream ◽  
Binary Form ◽  
Vehicle Classification ◽  
Source Image ◽  
Vehicle Speed ◽  
Method Of Determination

The paper presents a method of determination of gradient characteristics describing a detection field. The presented method is destined for image data. Image data are in the form of a source image sequence. Frames taken from a video stream, obtained at a measurement station, create a source image sequence. The same detection field is defined for all images of the source image sequence. The source image sequence is converted into binary form. Conversion is carried out on the basis of analysis of source images gradients. Layout of obtained binary vales of target images is in accordance with a content of source images. In the area of detection field, arithmetic and averaging sums of binary values are appropriately calculated. On the bases of averaging sums of binary values, gradient characteristics of the detection field are determined. Gradient characteristics of detection field are intended for vehicle detection and also can be utilized for vehicle speed determination or vehicle classification..

scholarly journals Optimal Relay Network for Aerial Remote Inspections

2021 ◽  
Author(s):  
Luis Ramos Pinto ◽  
Luis Almeida
Keyword(s):  
Video Stream ◽  
Digital Data ◽  
Relay Network ◽  
Delivery Ratio ◽  
Industrial Facilities ◽  
Data Packets ◽  
Access Structures ◽  
Communication Links ◽  
End To End ◽  
Remote Inspection

Unmanned Aerial Vehicles (UAVs) in particular multirotors are becoming the {\it de facto} tool for aerial sensing and remote inspection. In large industrial facilities, a UAV can transmit an online video stream to inspect difficult to access structures, such chimneys, deposits and towers. However, the communication range is limited, constraining the UAV operation range. This limitation can be overcome with relaying UAVs placed between the source UAV and the control station, creating a line of communication links. In this work we assume the use of a digital data packet network technology, namely WiFi, and tackle the problem of defining the exact placement for the relaying UAVs that creates an end-to-end channel with maximal delivery of data packets. We consider asymmetric communication links and we show an increase as large as $15$\% in end-to-end packet delivery ratio when compared to an equidistant placement. We also discuss the deployment of such a network and propose a fully distributed method that converges to the global optimal relay positions taking, on average, 1.4 the time taken by a centralized method.

scholarly journals An end-to-end convolutional network for joint detecting and denoising adversarial perturbations in vehicle classification

2021 ◽  
Author(s):  
Peng Liu ◽  
Huiyuan Fu ◽  
Huadong Ma
Keyword(s):  
Classification Model ◽  
Vehicle Classification ◽  
Joint Detection ◽  
Deep Convolutional Neural Networks ◽  
Convolutional Network ◽  
Processing Step ◽  
Adversarial Examples ◽  
Adversarial Attack ◽  
End To End ◽  
Public Datasets

AbstractDeep convolutional neural networks (DCNNs) have been widely deployed in real-world scenarios. However, DCNNs are easily tricked by adversarial examples, which present challenges for critical applications, such as vehicle classification. To address this problem, we propose a novel end-to-end convolutional network for joint detection and removal of adversarial perturbations by denoising (DDAP). It gets rid of adversarial perturbations using the DDAP denoiser based on adversarial examples discovered by the DDAP detector. The proposed method can be regarded as a pre-processing step—it does not require modifying the structure of the vehicle classification model and hardly affects the classification results on clean images. We consider four kinds of adversarial attack (FGSM, BIM, DeepFool, PGD) to verify DDAP’s capabilities when trained on BIT-Vehicle and other public datasets. It provides better defense than other state-of-the-art defensive methods.

Surgical management of extracranial carotid artery aneurysms

VASA ◽  
2016 ◽  
Vol 45 (3) ◽  
pp. 223-228 ◽  
Author(s):  
Jan Paweł Skóra ◽  
Jacek Kurcz ◽  
Krzysztof Korta ◽  
Przemysław Szyber ◽  
Tadeusz Andrzej Dorobisz ◽  
...  
Keyword(s):  
Carotid Artery ◽  
Surgical Management ◽  
Minor Stroke ◽  
Stroke Rate ◽  
Extracranial Carotid Artery ◽  
Long Term Follow Up ◽  
End To End ◽  
Dacron Patch ◽  
Major Stroke

Abstract. Background: We present the methods and results of the surgical management of extracranial carotid artery aneurysms (ECCA). Postoperative complications including early and late neurological events were analysed. Correlation between reconstruction techniques and morphology of ECCA was assessed in this retrospective study. Patients and methods: In total, 32 reconstructions of ECCA were performed in 31 symptomatic patients with a mean age of 59.2 (range 33 - 84) years. The causes of ECCA were divided among atherosclerosis (n = 25; 78.1 %), previous carotid endarterectomy with Dacron patch (n = 4; 12.5 %), iatrogenic injury (n = 2; 6.3 %) and infection (n = 1; 3.1 %). In 23 cases, intervention consisted of carotid bypass. Aneurysmectomy with end-to-end suture was performed in 4 cases. Aneurysmal resection with patching was done in 2 cases and aneurysmorrhaphy without patching in another 2 cases. In 1 case, ligature of the internal carotid artery (ICA) was required. Results: Technical success defined as the preservation of ICA patency was achieved in 31 cases (96.9 %). There was one perioperative death due to major stroke (3.1 %). Two cases of minor stroke occurred in the 30-day observation period (6.3 %). Three patients had a transient hypoglossal nerve palsy that subsided spontaneously (9.4 %). At a mean long-term follow-up of 68 months, there were no major or minor ipsilateral strokes or surgery-related deaths reported. In all 30 surviving patients (96.9 %), long-term clinical outcomes were free from ipsilateral neurological symptoms. Conclusions: Open surgery is a relatively safe method in the therapy of ECCA. Surgical repair of ECCAs can be associated with an acceptable major stroke rate and moderate minor stroke rate. Complication-free long-term outcomes can be achieved in as many as 96.9 % of patients. Aneurysmectomy with end-to-end anastomosis or bypass surgery can be implemented during open repair of ECCA.

Reliability of the Mangled Extremity Severity Score in the Management of Peripheral Vascular Injuries in Children: A Retrospective Review

2020 ◽  
Author(s):  
Ahmed Mousa ◽  
Ossama M. Zakaria ◽  
Mai A. Elkalla ◽  
Lotfy A. Abdelsattar ◽  
Hamad Al-Game'a
Keyword(s):  
Severity Score ◽  
Vein Graft ◽  
Bypass Surgery ◽  
Age Groups ◽  
Vascular Injuries ◽  
Peripheral Vascular ◽  
Group Iii ◽  
Mangled Extremity ◽  
Group I ◽  
End To End

AbstractThis study was aimed to evaluate different management modalities for peripheral vascular trauma in children, with the aid of the Mangled Extremity Severity Score (MESS). A single-center retrospective analysis took place between 2010 and 2017 at University Hospitals, having emergencies and critical care centers. Different types of vascular repair were adopted by skillful vascular experts and highly trained pediatric surgeons. Patients were divided into three different age groups. Group I included those children between 5 and 10 years; group II involved pediatrics between 11 and 15 years; while children between 16 and 21 years participated in group III. We recruited 183 children with peripheral vascular injuries. They were 87% males and 13% females, with the mean age of 14.72 ± 04. Arteriorrhaphy was performed in 32%; end-to-end anastomosis and natural vein graft were adopted in 40.5 and 49%, respectively. On the other hand, 10.5% underwent bypass surgery. The age groups I and II are highly susceptible to penetrating trauma (p = 0.001), while patients with an extreme age (i.e., group III) are more susceptible to blunt injury (p = 0.001). The MESS has a significant correlation to both age groups I and II (p = 0.001). Vein patch angioplasty and end-to-end primary repair should be adopted as the main treatment options for the repair of extremity vascular injuries in children. Moreover, other treatment modalities, such as repair with autologous vein graft/bypass surgery, may be adopted whenever possible. They are cost-effective, reliable, and simple techniques with fewer postoperative complication, especially in poor/limited resources.

Framework for rare event detection using Artificial Neural Network based context free grammar

2020 ◽  
Vol 39 (6) ◽  
pp. 8463-8475
Author(s):  
Palanivel Srinivasan ◽  
Manivannan Doraipandian
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Event Detection ◽  
Performance Metrics ◽  
Rare Events ◽  
Rare Event ◽  
Video Stream ◽  
Context Free Grammar ◽  
Artificial Neural ◽  
Context Free

Rare event detections are performed using spatial domain and frequency domain-based procedures. Omnipresent surveillance camera footages are increasing exponentially due course the time. Monitoring all the events manually is an insignificant and more time-consuming process. Therefore, an automated rare event detection contrivance is required to make this process manageable. In this work, a Context-Free Grammar (CFG) is developed for detecting rare events from a video stream and Artificial Neural Network (ANN) is used to train CFG. A set of dedicated algorithms are used to perform frame split process, edge detection, background subtraction and convert the processed data into CFG. The developed CFG is converted into nodes and edges to form a graph. The graph is given to the input layer of an ANN to classify normal and rare event classes. Graph derived from CFG using input video stream is used to train ANN Further the performance of developed Artificial Neural Network Based Context-Free Grammar – Rare Event Detection (ACFG-RED) is compared with other existing techniques and performance metrics such as accuracy, precision, sensitivity, recall, average processing time and average processing power are used for performance estimation and analyzed. Better performance metrics values have been observed for the ANN-CFG model compared with other techniques. The developed model will provide a better solution in detecting rare events using video streams.

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