Vision based surveillance system

Attard, Leanne; Farrugia, Reuben A.

doi:10.1109/eurocon.2011.5929144

Cited by 11 publications

(4 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Attard, Leanne et al, discussed implementation of a Vision based Surveillance System .This system is capable to detect and classify vehicles at single lane or normal surveillance videos with high accuracy. The proposed work based on the static background subtraction method [14]. However, this system is not applicable for the vehicle detection and classification for junctions and intersection's videos surveillance system at heavy urban traffic.…”

Section: Related Workmentioning

confidence: 99%

Ghost Vehicle and Shadow Removal Approach for Traffic Surveillance and Monitoring at Various Intersections Using Computer Vision

Hashmi

Keskar

Sai³

et al. 2015

IJMUE

View full text Add to dashboard Cite

As traffic surveillance technology continues to grow worldwide vehicle detection, counting, tracking and classification are gaining importance. This paper proposes computer vision based real time vehicle detection, tracking and classification at urban intersections. Firstly, foreground extraction using double subtraction method is proposed, which increases the accuracy of blob detection. Classification based on the geometrical attributes of the vehicle and also quadrant division of the junction is put forward. Setting up dynamic ROIs is discussed, which increased the scope of traffic surveillance for different types of junctions. The proposed system is implemented using Intel Open CV library for image processing and video processing applications. The Practical implementation of the algorithm is made with C++ and computer vision. Several junction surveillance videos are used to evaluate the performance of the traffic surveillance system. In this paper, detection, tracking and classification of objects in with removal of shadow and ghost vehicles at different junction in video surveillance .Proposed work elaborated computer vision approach for Traffic monitoring in traffic surveillance application. Test results in the performance of the proposed algorithm in detection, classification and counting and proved the effectiveness of the traffic surveillance system. Obtained results showed a better performance in terms of accuracy.

show abstract

Section: Related Workmentioning

confidence: 99%

Ghost Vehicle and Shadow Removal Approach for Traffic Surveillance and Monitoring at Various Intersections Using Computer Vision

Hashmi

Keskar

Sai³

et al. 2015

IJMUE

View full text Add to dashboard Cite

show abstract

“…Задача обнаружения объектов интереса и аномалий на видеопоследовательностях и отдельных изображениях остается актуальной уже не одно десятилетие, и число приложений, где она возникает, только возрастает. Среди них приложения, связанные с дистанционным зондированием Земли [1,2], наземным мониторингом [3,4], медицинскими исследованиями [5,6], радиолокацией [7,8], сельским хозяйством [9,10], рентгеновским сканированием багажа [11,12] и многие другие. Для обнаружения объектов интереса используется различная априорная информация вплоть до заданной библиотеки эталонов, а под аномалией (злокачественные образования, появление артефактов на видеопоследовательности и пр.…”

Section: Introductionunclassified

Detection of objects in the images: from likelihood relationships towards scalable and efficient neural networks

2022

View full text Add to dashboard Cite

The relevance of the tasks of detecting and recognizing objects in images and their sequences has only increased over the years. Over the past few decades, a huge number of approaches and methods for detecting both anomalies, that is, image areas whose characteristics differ from the predicted ones, and objects of interest, about the properties of which there is a priori information, up to the library of standards, have been proposed. In this work, an attempt is made to systematically analyze trends in the development of approaches and detection methods, reasons behind these developments, as well as metrics designed to assess the quality and reliability of object detection. Detection techniques based on mathematical models of images are considered. At the same time, special attention is paid to the approaches based on models of random fields and likelihood ratios. The development of convolutional neural networks intended for solving the recognition problems is analyzed, including a number of pre-trained architectures that provide high efficiency in solving this problem. Rather than using mathematical models, such architectures are trained using libraries of real images. Among the characteristics of the detection quality assessment, probabilities of errors of the first and second kind, precision and recall of detection, intersection by union, and interpolated average precision are considered. The paper also presents typical tests that are used to compare various neural network algorithms.

show abstract

“…A target with a bounding box is given for the very first frame of the video, and the model predicts the object'slocationwithheight and width in the subsequent frames. VOT has a wide range of vision-based applications, such as intelligent surveillance [1], autonomous vehicles [2], game analysis [3], and human-computer interface [4]. However, it remains a complicated process due to numerous nontrivial challenging aspects, including background clutter, occlusion, fast motion, motion blur, deformation, and illumination variation.…”

Section: Introductionmentioning

confidence: 99%

Siamese-Based Attention Learning Networks for Robust Visual Object Tracking

Rahman¹,

Jung²

2022

Artificial Intelligence

View full text Add to dashboard Cite

Tracking with the siamese network has recently gained enormous popularity in visual object tracking by using the template-matching mechanism. However, using only the template-matching process is susceptible to robust target tracking because of its inability to learn better discrimination between target and background. Several attention-learning are introduced to the underlying siamese network to enhance the target feature representation, which helps to improve the discrimination ability of the tracking framework. The attention mechanism is beneficial for focusing on the particular target feature by utilizing relevant weight gain. This chapter presents an in-depth overview and analysis of attention learning-based siamese trackers. We also perform extensive experiments to compare state-of-the-art methods. Furthermore, we also summarize our study by highlighting the key findings to provide insights into future visual object tracking developments.

show abstract

Vision based surveillance system

Cited by 11 publications

References 7 publications

Ghost Vehicle and Shadow Removal Approach for Traffic Surveillance and Monitoring at Various Intersections Using Computer Vision

Ghost Vehicle and Shadow Removal Approach for Traffic Surveillance and Monitoring at Various Intersections Using Computer Vision

Detection of objects in the images: from likelihood relationships towards scalable and efficient neural networks

Siamese-Based Attention Learning Networks for Robust Visual Object Tracking

Contact Info

Product

Resources

About