Unsupervised dense crowd detection by multiscale texture analysis
نویسندگان
چکیده
This study introduces a totally unsupervised method for the detection and location of dense crowds in images without context-awareness. With the perspective of setting up fully autonomous video-surveillance systems, automatic detection and location of crowds is a crucial step that is going to point which areas of the image have to be analyzed. After retrieving multiscale texturerelated feature vectors from the image, a binary classification is conducted to determine which parts of the image belong to the crowd and which to the background. The algorithm presented can be operated on images without any prior knowledge of any kind and is totally unsupervised.
منابع مشابه
Texture Synthesis and Unsupervised Recognition with Nonparametric Multiscale Markov Random Field Models
In this paper we present noncausal, nonparametric, multiscale, Markov Random Field (MRF) models for synthesising and recognising texture. The models have the ability to capture the characteristics of a wide variety of textures, varying from the structured to the stochastic. For texture synthesis, we use our own novel multiscale approach, incorporating local annealing, allowing us to use large n...
متن کاملUnsupervised detection and localization of structural textures using projection profiles
The main goal of existing approaches for structural texture analysis has been the identification of repeating texture primitives and their placement patterns in images containing a single type of texture. We describe a novel unsupervised method for simultaneous detection and localization of multiple structural texture areas along with estimates of their orientations and scales in real images. F...
متن کاملTexture synthesis and unsupervised recognition with a nonparametric multiscale Markov random field model
In this paper we present noncausal, nonparametric, multiscale, Markov Random Field (MRF) model for synthesising and recognising texture. The model has the ability to capture the characteristics of a wide variety of textures, varying from the structured to the stochastic. For texture synthesis, we use our own novel multiscale approach, incorporating local annealing, allowing us to use large neig...
متن کاملAnalysis and Comparison of Functional Dependencies of Multiscale Textural Features on Monospectral Infrared Images
In this paper, we deal with the problem of extracting meaningful textural features leading to good segmentations on satellite images of natural environments. Standard texture features using graylevel co-occurrence matrices have been widely applied on remote sensed images but they impose limitations (due to finite window sizes) as poor spatial localization. We have generalized the definition of ...
متن کاملWavelet-based Feature Analysis for Classification of Breast Masses from Normal Dense Tissue
Automated detection of masses on mammograms is challenged by the presence of dense breast parenchyma. The aim of this study was to investigate the feasibility of using wavelet-based feature analysis for differentiating masses, of varying sizes, from normal dense tissue on mammograms. The dataset analyzed consists of 166 regions of interest (ROIs) containing spiculated masses (60), circumscribed...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
- Pattern Recognition Letters
دوره 44 شماره
صفحات -
تاریخ انتشار 2014