Real-time control of video surveillance systems with program supervision techniques

Georis, B.; Brémond, François; Thonnat, Monique

doi:10.1007/s00138-006-0053-z

Cited by 12 publications

(6 citation statements)

References 18 publications

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“…The method combines the actions in to number of sequences and based on the similarity of motion sequences the classification is performed. A knowledge based approach for controlling video surveillance has been presented in [12]. The model has leaned features of classes according to the features considered.…”

Section: Shana L C Seldev Christophermentioning

confidence: 99%

Multi Variant Feature Similarity Based Behavior Tracking in Video Surveillance using Ann

L*¹,

Christopher²

2019

IJRTE

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The problem of video surveillance has been well studied which has been adapted for several issues. The behavior of any human can be monitored through video surveillance. There are number of approaches available for the video surveillance and behavior analysis. The previous methods uses background models, object tracking for the problem of behavior analysis. The methods suffer with poor accuracy in behavior analysis. To improve the performance, a multi variant feature similarity model based behavior tracking in video surveillance is presented. The method involves in identifying interest points throughout the images of video. Second, the changing feature has been identified to measure the multi variant feature similarity by using multi variant feature model. Based on the MVFS, the object tracking is performed. The human tracking is performed in the same way and the multi variant features are trained with artificial neural network which has number of behavior classes. At the testing phase, the video has been removed with background features according to the multi feature model adapted. Once the object has been identified, then tracking and behavior analysis is performed by measuring MVFS with the features at different behavior classes. The artificial neural network has been used for the classification of behavior identified through video surveillance. The method would produce higher accuracy and improves the performance.

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Section: Shana L C Seldev Christophermentioning

confidence: 99%

Multi Variant Feature Similarity Based Behavior Tracking in Video Surveillance using Ann

L*¹,

Christopher²

2019

IJRTE

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“…The authors in [Georis et al, 2007] present a controlled video understanding system based on a knowledge base. The system is composed of three main components in which the control component performs several steps for managing all the online processes of the system (e.g program execution and automatic parameter tuning).…”

Section: State Of the Artmentioning

confidence: 99%

Online parameter tuning for object tracking algorithms

Chau

Thonnat

Brémond

et al. 2014

Image and Vision Computing

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Object tracking quality usually depends on video scene conditions (e.g. illumination, density of objects, object occlusion level). In order to overcome this limitation, this article presents a new control approach to adapt the object tracking process to the scene condition variations. More Each context cluster is then associated to satisfactory tracking parameters. In the online control phase, once a context change is detected, the tracking parameters are tuned using the learned values. The approach has been experimented with three different tracking algorithms and on long, complex video datasets. This article brings two significant contributions: (1) a classification method of video sequences to learn offline tracking parameters, (2) a new method to tune online tracking parameters using tracking context.

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“…Similarly, [3] described an approach tailored to detect events for the soccer domain. Furthermore, [16] presented a knowledge-based controlled platform for video event analysis. However, algorithm selection is performed by the user and optimum algorithm selection is modeled as ne tuning using ground-truth data.…”

Section: Automatic Controlmentioning

confidence: 99%

“…Nevertheless, their design is based on a human operator who has to accumulate a great amount of experience related with video processing, network design, data management and so on. To simplify this task, several approaches have been proposed based on performance evaluation [19], available resources [25] and knowledge descriptions [16]. However, they are not fully automatic requiring the human intervention in most of the design stages.…”

Section: Introductionmentioning

confidence: 99%