Synoptic video based human crowd behavior analysis for forensic video surveillance

Yogameena, B.; Priya, K. Sindhu

doi:10.1109/icapr.2015.7050662

Cited by 22 publications

(8 citation statements)

References 6 publications

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“…Nesne hedefli çalışmalarda ise terk edilmiş paket tespiti önemli bir yer tutmaktadır. Kalabalık için anomali tespiti ise video gözetim sistemlerinde en yaygın konulardan biridir [14][15][16][17].…”

Section: Anomali Tespiti Ve Gözetimunclassified

Video Gözetim Sistemlerinde Anomali Tespiti Üzerine Bir Derleme

Öz¹,

Görgünoğlu²

2016

El-Cezeri Fen Ve Mühendislik Dergisi

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Günümüzde yaygın olarak kullanılan kameralar otomatik gözetim sistemlerinin gelişmesine katkıda bulunmuştur. Gözetim sistemleri ile birlikte video görüntülerinde olağandışı durumların tespiti çalışmalarına olan ilgi artmıştır. Anomali olarak da isimlendirilebilen bu durumlar başta güvenlik olmak üzere pek çok alanda kullanılmaktadır. Bu çalışmada video görüntülerinde anomali tespiti ve ilgili çalışmalar incelenmiştir. Anomali tespiti ve gözetim, özellik çıkarımı, eğitim ve öğrenme, modelleme ve sınıflandırma algoritmaları üzerinde durulmuştur.Anahtar kelimeler: Video analizi, anomali tespiti, video gözetim sistemleri, anomali tespit yöntemleri.

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Section: Anomali Tespiti Ve Gözetimunclassified

Video Gözetim Sistemlerinde Anomali Tespiti Üzerine Bir Derleme

Öz¹,

Görgünoğlu²

2016

El-Cezeri Fen Ve Mühendislik Dergisi

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“…The care must be taken by introducing the vigilance cameras which discovers the abnormal behavior in crowd undoubtedly [4]. The Researchers are completely focused towards this abnormal detection, an important feature of the crowd scene analysis.…”

Section: Abnormal Crowd Behavior Detection Using Structural Context Dmentioning

confidence: 99%

Abnormal Crowd behavior Detection Using Structural Context Descriptor

Pooja¹,

Vishwakarma²

2016

BIJAIP

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In concern with the people safety in public places, an Abnormal Behavior Detection in crowded scene has gained popularity. The Abnormal Behavior Detection is an active area of research. This has gained a particular interest amongst the researchers in this evolving era of computer science. For this purpose the system proposes the structure context descriptor (SCD) for describing the crowd individual, which represents the relationship among the individuals. The approach presents the abnormal behavior detection method from the crowd scenes to efficiently detect anomalies present in the crowded scenes.

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“…Automated analysis of security video sequences is still an open issue that demands adaptation of existing technology to cope with increasing challenges, such as the ones mentioned in the previous section, e.g., low image quality, lack of training samples, etc. Some of the directions and research topics in video surveillance systems include: detection, categorization and tracking of objects of interest in video (e.g., people, vehicles, abandoned baggages) [8,23,18], recognition of their activities (e.g., event and behavior analysis) [22], and efficient technologies to process large amounts of data (Big Data) [7]. Generally all these tasks more or less follow a basic framework.…”

Section: Related Workmentioning

confidence: 99%

Retrieval of Distinctive Regions of Interest from Video Surveillance Footage: A Real Use Case Study

Mitrea¹,

Piatrik²,

Neville³

2015

6th International Conference on Imaging for Crime Prevention and Detection (ICDP-15)

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The article addresses the issue of retrieving distinctive regions of interest or patterns (DROP) in video surveillance datasets. DROP may include logos, tattoos, color regions or any other distinctive features that appear recorded on video. These data come in particular with specific difficulties such as low image quality, multiple image perspectives, variable lighting conditions and lack of enough training samples. This task is a real need functionality as the challenges are derived from practice of police forces. We present our preliminary results on tackling such scenario from Scotland Yard, dealing with the constraints of a real world use case. The proposed method is based on two approaches: employment of a dense SIFT-based descriptor (Pyramidal Histogram of Visual Words), and use of image segmentation (Mean-Shift) with feature extraction on each segment computed. Tested on real data we achieve very promising results that we believe will contribute further to the ground development of advanced methods to be applied and tested in real forensics investigations.

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Synoptic video based human crowd behavior analysis for forensic video surveillance

Cited by 22 publications

References 6 publications

Video Gözetim Sistemlerinde Anomali Tespiti Üzerine Bir Derleme

Video Gözetim Sistemlerinde Anomali Tespiti Üzerine Bir Derleme

Abnormal Crowd behavior Detection Using Structural Context Descriptor

Retrieval of Distinctive Regions of Interest from Video Surveillance Footage: A Real Use Case Study

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