Detection of anomalous events in shipboard video using moving object segmentation and tracking

Wenger, Ben; Mandayam, Shreekanth; Violante, Patrick J.; Drake, K.

doi:10.1109/autest.2010.5613544

Cited by 4 publications

(2 citation statements)

References 12 publications

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“…2(c-d). Although, the proposed method relies on localised importance estimated across blocks of regions; estimating importances using other low-level features such as the use of context [58], trajectory density [11,15], motion [59,60], spatio-temporal scene structure [61] are also possible.…”

Section: Motion Anomaly Detectionmentioning

confidence: 99%

Motion anomaly detection and trajectory analysis in visual surveillance

Chebiyyam

Reddy

Dogra

et al. 2017

Multimed Tools Appl

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Motion anomaly detection through video analysis is important for delivering autonomous situation awareness in public places. Surveillance scene segmentation and representation is the preliminary step to implementation anomaly detection. Surveillance scene can be represented using Region Association Graph (RAG), where nodes represent regions and edges denote connectivity among the regions. Existing RAG-based analysis algorithms assume simple anomalies such as moving objects visit statistically unimportant or abandoned regions. However, complex anomalies such as an object encircles within a particular region (Type-I) or within a set of regions (Type-II). In this paper, we extract statistical features from a given set of object trajectories and train multi-class support vector machines (SVM) to deal with each type of anomaly. In the testing phase, a given test trajectory is categorized as normal or anomalous with respect to the trained models. Performance evaluation of the proposed algorithm has been carried out on public as well as our own datasets. We have recorded sensitivity as high as 86% and fallout rate as low as 9% in experimental evaluation of the proposed technique. We have carried out comparative analysis with state-of-the-art techniques to benchmark the method. It has been observed that the proposed model is consistent and highly accurate across challenging datasets.

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Section: Motion Anomaly Detectionmentioning

confidence: 99%

Motion anomaly detection and trajectory analysis in visual surveillance

Chebiyyam

Reddy

Dogra

et al. 2017

Multimed Tools Appl

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“…Gauss karışım modeli (Gaussian mixture model (GMM)) iyi tanımlanmış bir denetimsiz kümeleme yöntemidir. Görüntü işlemede sıklıkla kullanılan yöntem anomali tespitinde de kullanılmaktadır [17,29,35,[42][43][44][45].…”

Section: Kümelemeunclassified

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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