Multi-Stage Video Analysis Framework

Czyżewski, Andrzej; Szwoch, Grzegorz; Dalka, Piotr; Szczuko, Piotr; Ciarkowski, Andrzej; Ellwart, Damian; Merta, Tomasz; Lopatka, Kuba; Kulasek, Łukasz; Wolski, Jędrzej

doi:10.5772/16088

Cited by 18 publications

(15 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This separation is necessary in order to select the pixels representing the actual moving objects for the purpose of object detection, tracking, etc. [5]. The background modeling is usually performed by constructing a background model, which may be based, e.g., on the statistical analysis of the pixel values.…”

Section: Background Subtraction Algorithmsmentioning

confidence: 99%

See 1 more Smart Citation

Parallel implementation of background subtraction algorithms for real-time video processing on a supercomputer platform

Szwoch

Ellwart

Czyżewski

2012

J Real-Time Image Proc

Self Cite

View full text Add to dashboard Cite

Results of evaluation of the background subtraction algorithms implemented on a supercomputer platform in a parallel manner are presented in the article. The aim of the work is to chose an algorithm, a number of threads and a task scheduling method, that together provide satisfactory accuracy and efficiency of a real-time processing of high-resolution camera images, maintaining the cost of resources usage at a reasonable level. Two selected algorithms: the Gaussian mixture models and the Codebook, are presented and their computational complexity is discussed. Various approaches to the parallel implementation, including assigning the image pixels to threads, the task scheduling methods and the thread management systems, are presented. The experiments were performed on a supercomputer cluster, using a single machine with 12 physical cores. The accuracy and performance of the implemented algorithms were evaluated for varying image resolutions and numbers of concurrent processing threads. On a basis of the evaluation results, an optimal configuration for the parallel implementation of the system for real-time video content analysis on a supercomputer platform was proposed.

show abstract

Section: Background Subtraction Algorithmsmentioning

confidence: 99%

“…This is one of the most frequently used algorithms in video content analysis frameworks for a detection and tracking of moving objects and an automatic event detection [5]. At the same time, it is one of the most computationally intensive image analysis procedures.…”

Section: Introductionmentioning

confidence: 99%

Parallel implementation of background subtraction algorithms for real-time video processing on a supercomputer platform

Szwoch

Ellwart

Czyżewski

2012

J Real-Time Image Proc

Self Cite

View full text Add to dashboard Cite

show abstract

“…Tracking objects in a single camera is based on visual features of a moving object which differ from a background of a video image [1], [2]. Unfortunately such an approach is not suitable for the posed problem of re-identification of the same object in two different cameras.…”

Section: Introductionmentioning

confidence: 99%

Pawlak's flow graph extensions for video surveillance systems

Lisowski¹,

Czyżewski²

2015

Annals of Computer Science and Information Systems

View full text Add to dashboard Cite

Abstract-The idea of the Pawlak's flow graphs is applicable to many problems in various fields related to decision algorithms or data mining. The flow graphs can be used also in the video surveillance systems. Especially in distributed multi-camera systems which are problematic to be handled by human operators because of their limited perception. In such systems automated video analysis needs to be implemented. Important part of this analysis is tracking object within a single camera and between cameras' fields of vision. One of element needed to re-identify the single real object besides object's visual features and spatiotemporal dependencies between cameras is a behaviour model. The flow graph after some modifications, is a suitable data structure, which concept is based on the rough set theory, to contained as a behaviour model in it. Additionally, the flow graph can be used to predict the future movement of given object. In this paper a survey of authors research works related to employing flowgraphs in video surveillance systems is contained. The flow graph creation based on the paths of objects inside supervised area will presented. Moreover, a method of building a probability tree on the basis of the flow graph and a method for adapting the flowgraph to the changing topology of the camera network are also discussed.

show abstract

“…[7]. This complex, modular system performs video analysis from the low-level pixel-based image analysis to the interpretation of video content and decision making.…”

mentioning

confidence: 99%

“…The proposed approach is conceptually easy, it is able to perform online processing, it handles short-term occlusions and it is separated from the background subtraction procedure. Second, the paper shows that the proposed algorithm provides data that may be used for the task of unattended luggage detection in a modular, multi-stage video analysis system [7]. The main focus of the paper is to present the algorithm for detection of stationary objects, but in order to demonstrate that this algorithm provides data useful for efficient unattended luggage detection, a working system, in which the proposed algorithm is supplemented with the classification and decision modules (implemented in a simplified way for evaluation purposes) is presented and discussed.…”

mentioning

confidence: 99%

Extraction of stable foreground image regions for unattended luggage detection

Szwoch

2014

Multimed Tools Appl

Self Cite

View full text Add to dashboard Cite

A novel approach to detection of stationary objects in the video stream is presented. Stationary objects are these separated from the static background, but remaining motionless for a prolonged time. Extraction of stationary objects from images is useful in automatic detection of unattended luggage. The proposed algorithm is based on detection of image regions containing foreground image pixels having stable values in time and checking their correspondence with the detected moving objects. In the first stage of the algorithm, stability of individual pixels belonging to moving objects is tested using a model constructed from vectors. Next, clusters of pixels with stable color and brightness are extracted from the image and related to contours of the detected moving objects. This way, stationary (previously moving) objects are detected. False contours of objects removed from the background are also found and discarded from the analysis. The results of the algorithm may be analyzed further by the classifier, separating luggage from other objects, and the decision system for unattended luggage detection. The main focus of the paper is on the algorithm for extraction of stable image regions. However, a complete framework for unattended luggage detection is also presented in order to show that the proposed approach provides data for successful event detection. The results of experiments in which the proposed algorithm was validated using both standard datasets and video recordings from a real airport security system are presented and discussed.

show abstract

Multi-Stage Video Analysis Framework

Cited by 18 publications

References 24 publications

Parallel implementation of background subtraction algorithms for real-time video processing on a supercomputer platform

Parallel implementation of background subtraction algorithms for real-time video processing on a supercomputer platform

Pawlak's flow graph extensions for video surveillance systems

Extraction of stable foreground image regions for unattended luggage detection

Contact Info

Product

Resources

About