A Framework for Analysis of Surveillance Videos

Choudhary, Ayesha; Chaudhury, Santanu; Banerjee, Subhashis

doi:10.1109/icvgip.2008.76

Cited by 9 publications

(7 citation statements)

References 7 publications

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“…Detecting unusual activities is important for automated surveillance. In the paper [7], Ayesha Choudhary et.al. proposed a new framework for automatic analysis of surveillance videos.…”

Section: B Human Anomalies Detection In Surveillance Sitesmentioning

confidence: 99%

Frameworks for Animal and Human Detection using Camera Images to Detect Anomaly

P*¹,

Krishnakumar²

2019

IJITEE

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Human and animal interacting events in video image frames will leads to anomalies and it cannot be predicted. These anomalous events was happened due to the interactions of human, animals and birds with each other. Some of the human and animal anamolous interactions will leads to anomalous actions in camera surveillance sites and are to be considered as a serious issue. All anomalies will leads the system or the authorities who dedicated for monitoring to the suspicious events. To detect the anomalies, the animals and human objects must be identified in each image frames first. After object identification events detection phase has to be done. Before going to the event detection phase, noise elimination, shadow detection, object classification can be carried out according to the needs. This paper reveals general detection methodology for animal detection and human crowd. The proposed work mainly concentrated on the detection of animals in human territory based on its palm print images and video images. Human crowd can be treated as smoke- screen for all types of anomalies and so this paper mainly concentrated to detect the crowd well in advance and thereby preventing the crowd before it is happening

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“…Detecting unusual activities is important for automated surveillance. In the paper [7], Ayesha Choudhary et.al. proposed a new framework for automatic analysis of surveillance videos.…”

Section: B Human Anomalies Detection In Surveillance Sitesmentioning

confidence: 99%

Frameworks for Animal and Human Detection using Camera Images to Detect Anomaly

P*¹,

Krishnakumar²

2019

IJITEE

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“…This is the first step that was performed in most of the crowd detection model and a sample result is shown in figure 2. To detect the crowd event, some preprocessing works has to be carried out for all the video image frames [5].…”

Section: E General Framework For Crowd Modelingmentioning

confidence: 99%

“…Many techniques have been proposed for removing shadows from images. In the method proposed by Scanlan, et al [5], the input image is first divided into blocks. For each block, the mean intensity value of the block is computed.…”

Section: B Shadow Eliminationmentioning

confidence: 99%

Prediction of Crowd in Three Levels: a Foot Step to Anomalies Prevention

P*¹,

Krishnakumar²

2019

IJITEE

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Crowd is a huge number of people meet jointly in a disorganized or unmanageable way. Crowd in any atmosphere may leads to suspicious events. Nobody can predict the crowd and some anomalies might happen in the presence of crowd. So prevention of crowd well in advance is the only remedy to tackle the situation. Advance crowd detection is an important subfield in video surveillance. Prior detection or prevention of crowd has so much of importance while we are considering the present scenarios all over the world. So now-days, an automatic crowd prevention technique is needed for all the countries to protect their land, provide safety for their citizens and law enforcement. Crowd prevention system using manual operators are weak due to many physiological and non-physiological factors but it will provide better performance than automatic system in case of decision making. Many models have been developed so far to detect the crowd automatically. Our system aims to predict the crowd well in advance in three levels and so the automatic system or the operator will get enough time to respond or take a decision. To detect the formation of crowd well in advance, all the human objects in a frame was identified by Gaussian mixture model and object classification, shadow was eliminated and crowd was predicted using the object rectangle model and center vertical line model. The pixel distance between the each rectangles and center line is used to predict the formation of crowd. This paper also gives some suggestions to crowd modelling.

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“…Choudhary et al [18] represented the low-level features like the position of objects, size of objects, color correlogram of objects, and so on extracted from the frames of the video or high level semantically meaningful concepts like the object category and the object trajectory [18].…”

Section: Feature Extractionmentioning

confidence: 99%

“…Choudhary et al [18] proposed a framework for automated analysis of the surveillance videos using cluster algebra to mine various combinations of patterns from the component summaries (such as time, size, shape, position of objects, etc.) to learn the usual patterns of events and discover unusual ones.…”

Section: Raw Video Data Miningmentioning

confidence: 99%

A study on video data mining

Vijayakumar

Nedunchezhian²

2012

Int J Multimed Info Retr

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Data mining is a process of extracting previously unknown knowledge and detecting the interesting patterns from a massive set of data. Thanks to the extensive use of information technology and the recent developments in multimedia systems, the amount of multimedia data available to users has increased exponentially. Video is an example of multimedia data as it contains several kinds of data such as text, image, meta-data, visual and audio. It is widely used in many major potential applications like security and surveillance, entertainment, medicine, education programs and sports. The objective of video data mining is to discover and describe interesting patterns from the huge amount of video data as it is one of the core problem areas of the data-mining research community. Compared to the mining of other types of data, video data mining is still in its infancy. There are many challenging research problems existing with video mining. Beginning with an overview of the video datamining literature, this paper concludes with the applications of video mining.

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A Framework for Analysis of Surveillance Videos

Cited by 9 publications

References 7 publications

Frameworks for Animal and Human Detection using Camera Images to Detect Anomaly

Frameworks for Animal and Human Detection using Camera Images to Detect Anomaly

Prediction of Crowd in Three Levels: a Foot Step to Anomalies Prevention

A study on video data mining

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