An evaluation of different features and learning models for anomalous event detection


Autoria(s): Nallaivarothayan, Hajananth; Ryan, David; Denman, Simon; Sridharan, Sridha; Fookes, Clinton
Data(s)

01/11/2013

Resumo

The huge amount of CCTV footage available makes it very burdensome to process these videos manually through human operators. This has made automated processing of video footage through computer vision technologies necessary. During the past several years, there has been a large effort to detect abnormal activities through computer vision techniques. Typically, the problem is formulated as a novelty detection task where the system is trained on normal data and is required to detect events which do not fit the learned ‘normal’ model. There is no precise and exact definition for an abnormal activity; it is dependent on the context of the scene. Hence there is a requirement for different feature sets to detect different kinds of abnormal activities. In this work we evaluate the performance of different state of the art features to detect the presence of the abnormal objects in the scene. These include optical flow vectors to detect motion related anomalies, textures of optical flow and image textures to detect the presence of abnormal objects. These extracted features in different combinations are modeled using different state of the art models such as Gaussian mixture model(GMM) and Semi- 2D Hidden Markov model(HMM) to analyse the performances. Further we apply perspective normalization to the extracted features to compensate for perspective distortion due to the distance between the camera and objects of consideration. The proposed approach is evaluated using the publicly available UCSD datasets and we demonstrate improved performance compared to other state of the art methods.

Formato

application/pdf

Identificador

http://eprints.qut.edu.au/65906/

Publicador

IEEE

Relação

http://eprints.qut.edu.au/65906/1/my_dicta_paper.pdf

DOI:10.1109/DICTA.2013.6691480

Nallaivarothayan, Hajananth, Ryan, David, Denman, Simon, Sridharan, Sridha, & Fookes, Clinton (2013) An evaluation of different features and learning models for anomalous event detection. In International Conference on Digital Image Computing : Techniques and Applications (DICTA), IEEE, Wrest Point Hotel, Hobart, Tasmania, Australia, pp. 1-8.

Direitos

Copyright 2013 IEEE

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Fonte

School of Electrical Engineering & Computer Science; Science & Engineering Faculty

Palavras-Chave #080104 Computer Vision #080106 Image Processing #Abnormal event detection #Semi-2D HMM #GMM #optical flow #perspective normalization
Tipo

Conference Paper