http://cvssp-data.eps.surrey.ac.uk/Personal/AdrianHilton/Publications_files/survey_techrep.pdf

A survey of advances in vision-based human motion capture and analysis

Background subtraction is a common computer vision task. We analyze the usual pixel-level approach. We develop an efficient adaptive algorithm using Gaussian mixture probability density. Recursive equations are used to constantly update the parameters and but also to simultaneously select the appropriate number of components for each pixel.

Improved adaptive Gaussian mixture model for background subtraction

This paper presents a technique for motion detection that incorporates several innovative mechanisms. For example, our proposed technique stores, for each pixel, a set of values taken in the past at the same location or in the neighborhood. It then compares this set to the current pixel value in order to determine whether that pixel belongs to the background, and adapts the model by choosing randomly which values to substitute from the background model. This approach differs from those based upon the classical belief that the oldest values should be replaced first. Finally, when the pixel is found to be part of the background, its value is propagated into the background model of a neighboring pixel. We describe our method in full details (including pseudo-code and the parameter values used) and compare it to other background subtraction techniques. Efficiency figures show that our method outperforms recent and proven state-of-the-art methods in terms of both computation speed and detection rate. We also analyze the performance of a downscaled version of our algorithm to the absolute minimum of one comparison and one byte of memory per pixel. It appears that even such a simplified version of our algorithm performs better than mainstream techniques.

/pdf/vibe-a-universal-background-subtraction-algorithm-for-video-3hbi5ckurx.pdf

ViBe: A Universal Background Subtraction Algorithm for Video Sequences

Deep-learning (DL) algorithms, which learn the representative and discriminative features in a hierarchical manner from the data, have recently become a hotspot in the machine-learning area and have been introduced into the geoscience and remote sensing (RS) community for RS big data analysis. Considering the low-level features (e.g., spectral and texture) as the bottom level, the output feature representation from the top level of the network can be directly fed into a subsequent classifier for pixel-based classification. As a matter of fact, by carefully addressing the practical demands in RS applications and designing the input?output levels of the whole network, we have found that DL is actually everywhere in RS data analysis: from the traditional topics of image preprocessing, pixel-based classification, and target recognition, to the recent challenging tasks of high-level semantic feature extraction and RS scene understanding.

Deep Learning for Remote Sensing Data: A Technical Tutorial on the State of the Art

This paper describes a probabilistic approach to the problem of recognizing places based on their appearance. The system we present is not limited to localization, but can determine that a new observation comes from a previously unseen place, and so augment its map. Effectively this is a SLAM system in the space of appearance. Our probabilistic approach allows us to explicitly account for perceptual aliasing in the environment—identical but indistinctive observations receive a low probability of having come from the same place. We achieve this by learning a generative model of place appearance. By partitioning the learning problem into two parts, new place models can be learned online from only a single observation of a place. The algorithm complexity is linear in the number of places in the map, and is particularly suitable for online loop closure detection in mobile robotics.

/pdf/fab-map-probabilistic-localization-and-mapping-in-the-space-1yf91wf5w8.pdf

FAB-MAP: Probabilistic Localization and Mapping in the Space of Appearance

Efficient adaptive density estimation per image pixel for the task of background subtraction

There are two open problems when finite mixture densities are used to model multivariate data: the selection of the number of components and the initialization. In this paper, we propose an online (recursive) algorithm that estimates the parameters of the mixture and that simultaneously selects the number of components. The new algorithm starts with a large number of randomly initialized components. A prior is used as a bias for maximally structured models. A stochastic approximation recursive learning algorithm is proposed to search for the maximum a posteriori (MAP) solution and to discard the irrelevant components.

/pdf/recursive-unsupervised-learning-of-finite-mixture-models-3tqouc7cum.pdf

Recursive unsupervised learning of finite mixture models

The iterative procedure called 'mean-shift' is a simple robust method for finding the position of a local mode (local maximum) of a kernel-based estimate of a density function. A new robust algorithm is given here that presents a natural extension of the 'mean-shift' procedure. The new algorithm simultaneously estimates the position of the local mode and the covariance matrix that describes the approximate shape of the local mode. We apply the new method to develop a new 5-degrees of freedom (DOF) color histogram based non-rigid object tracking algorithm.

http://www.cse.psu.edu/~rtc12/CSE598G/papers/zivkovic2004CVPR.pdf

An EM-like algorithm for color-histogram-based object tracking

Vision systems are used more and more in 'personal' robots interacting with humans, since semantic information about objects and places can be derived from the rich sensory information. Visual information is also used for building appearance based topological maps, which can be used for localization. In this paper we describe a system capable of using this appearance based topological map for navigation. The system is made robust by using the epipolar geometry and a planar floor constraint in computing the necessary heading information. Using this method the robot is able to drive robustly in a large environment. We tested the method on real data under varying environment conditions and compared performance with a human-controlled robot.

http://vigir.missouri.edu/~gdesouza/Research/Conference_CDs/IEEE_ICRA_2007/data/papers/1109.pdf

Z. Zivkovic

Papers

Improved adaptive Gaussian mixture model for background subtraction

Efficient adaptive density estimation per image pixel for the task of background subtraction

Recursive unsupervised learning of finite mixture models

An EM-like algorithm for color-histogram-based object tracking

Navigation using an appearance based topological map