A method of estimating the number of people in crowded scenes was proposed based on energy of the foreground image in the frequency domain. Firstly, the foreground image including the target area was obtained by background subtraction. And furthermore, with the second order wavelet decomposition of the foreground image, the energy of high frequency sub-band was computed. Finally, by analyzing t...

Human head pose and gaze directions have traditionally been studied for expression and face recognition, and human computer interaction [6]. Accurate estimations of either head or gaze direction can provide useful information for the inference of a person’s intent and behaviour. However, most existing techniques rely upon medium to high resolution images captured under well controlled condition...

Situational awareness is a basic function of the human visual system, which is attracting a lot of research attention in machine vision and related research communities. There is an increasing demand for smarter video surveillance of public and private space using intelligent vision systems which can distinguish what is semantically meaningful to the human observer as ‘normal’ and ‘abnormal’ be...

When humans navigate a crowed space such as a university campus or the sidewalks of a busy street, they follow common sense rules based on social etiquette. In this paper, we argue that in order to enable the design of new algorithms that can take fully advantage of these rules to better solve tasks such as target tracking or trajectory forecasting, we need to have access to better data in the ...

We present results on the PETS 2009 dataset using surveillance systems based on holistic properties of the video. In particular, we evaluate a crowd counting system, based on regression of holistic (global) features, on the PETS 2009 dataset. We also present experimental results on crowd event detection when using the dynamic texture model to represent holistic motion flow in the video.

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This work addresses the problem of detecting human behavioural anomalies in crowded surveillance environments. We focus in particular on the problem of detecting subtle anomalies in a behaviourally heterogeneous surveillance scene. To reach this goal we implement a novel unsupervised context-aware process. We propose and evaluate a method of utilising social context and scene context to improve...

Crowd behavior analysis is an interdisciplinary topic. Understanding the collective crowd behaviors is one of the fundamental problems both in social science and natural science. Research of crowd behavior analysis can lead to a lot of critical applications, such as intelligent video surveillance, crowd abnormal detection, and public facility optimization. In this thesis, we study the crowd beh...

Video surveillance is one of the most studied application in Computer Vision. We propose a novel method to identify and track people in a complex environment with stereo cameras. It uses two stereo cameras to deal with occlusions, two different background models that handle shadows and illumination changes and a new segmentation algorithm that is effective in crowded environments. The algorithm...

This distribution addresses the problem of detecting pedestrians in crowded real-world scenes with severe overlaps. The basic premise is that this problem is too difficult for any type of model or feature alone. The first algorithm that integrates evidence in multiple iterations and from different sources proposed by Leibe et al. [2005] is presented. The core part of this method is the combinat...