Change detection by probabilistic segmentation from monocular view

Hernandez-Lopez, Francisco J.; Rivera, Mariano

doi:10.1007/s00138-013-0564-3

Cited by 22 publications

(9 citation statements)

References 40 publications

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“…6 methods are relatively simple as they rely on plain background subtraction, of which 2 use color features (Euclidean and Mahalanobis distance methods described in [5], [7]), one uses RGB histograms over time [19], one uses local self-similarity features [20], and one use a more complex model which implements a quadratic Markov measure field [26]. Two fairly old, but frequently-cited methods have also been tested: KDE-based estimation by Elgammal et al [8] and GMM by Stauffer and Grimson [29].…”

Section: Methods Testedmentioning

confidence: 99%

“…For example, Hermade-Lopez and Rivera [26] implement a background model designed to be robust to illumination changes, cast shadows and camouflage. As opposed to other basic methods which detect motion in a maximum likelihood matter (which is sensitive to noise and isolated artifacts) they implement a quadratic Markov measure field (QMMF) which enforces spacial regularity with the help of a quadratic programming solved.…”

Section: B Change Detectionmentioning

confidence: 99%

“…Running average [7], [18], [19], [20], [26] Temporal median [17] Motion history image [24], [25] Kalman filter [21], [22] Weiner filter [23] Parametric Single Gaussian [3] Gaussian Mixture Model (GMM) [29], [30], [31], [32], [35], [34], [36], [33] Background clustering [39], [40], [41] Generalized Gaussian Model [37], [27] Bayesian [13], [38] Chebyshev inequality [25] Non-Parametric & Data-driven Kernel Density Estimation (KDE) [8], [42], [43], [50] Cyclostationary [12] Stochastic K-nearest neighbors (KNN) [9], [45]. Deterministic KNN [32] Bayesian [33] Local binary patterns [47], [48] Hidden Markov Model (HMM) [46] Matrix Decomposition Principal Component Analysis (PCA) [51], [52], [53], [54], [55], [56], …”

Section: Basicmentioning

confidence: 99%

See 2 more Smart Citations

A Novel Video Dataset for Change Detection Benchmarking

Goyette

Jodoin

Porikli

et al. 2014

IEEE Trans. on Image Process.

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Change detection is one of the most commonly encountered low-level tasks in computer vision and video processing. A plethora of algorithms have been developed to date, yet no widely accepted, realistic, large-scale video data set exists for benchmarking different methods. Presented here is a unique change detection video data set consisting of nearly 90 000 frames in 31 video sequences representing six categories selected to cover a wide range of challenges in two modalities (color and thermal infrared). A distinguishing characteristic of this benchmark video data set is that each frame is meticulously annotated by hand for ground-truth foreground, background, and shadow area boundaries-an effort that goes much beyond a simple binary label denoting the presence of change. This enables objective and precise quantitative comparison and ranking of video-based change detection algorithms. This paper discusses various aspects of the new data set, quantitative performance metrics used, and comparative results for over two dozen change detection algorithms. It draws important conclusions on solved and remaining issues in change detection, and describes future challenges for the scientific community. The data set, evaluation tools, and algorithm rankings are available to the public on a website and will be updated with feedback from academia and industry in the future.

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Section: Methods Testedmentioning

confidence: 99%

Section: B Change Detectionmentioning

confidence: 99%

Section: Basicmentioning

confidence: 99%

See 1 more Smart Citation

A Novel Video Dataset for Change Detection Benchmarking

Goyette

Jodoin

Porikli

et al. 2014

IEEE Trans. on Image Process.

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“…A great variety of segmentation algorithms and its applications have been proposed in the literature [1,2,3,4,5]. In this paper we use the HyperConic Multilayer Perceptron (HC-MLP) [6] for color image segmentation, since the color image segmentation problem can be considered as a supervised pixel classification problem.…”

Section: Introductionmentioning

confidence: 99%

Color Image Segmentation with a Hyper-Conic Multilayer Perceptron

Serrano

Herrera

2014

Image and Video Technology

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Abstract. We apply the Hyper-Conic Artificial Multilayer Perceptron (HC-MLP) to color image segmentation, where we consider image segmentation as a classification problem distinguishing between foreground and background pixels. The HC-MLP was designed by using the conic space and conformal geometric algebra. The neurons in the hidden layer contain a transfer function that defines a quadratic surface (spheres, ellipsoids, paraboloids and hyperboloids) by means of inner and outer products, and the neurons in the output layer contain a transfer function that decides whether a point is inside or outside a sphere. The Particle Swarm Optimization algorithm (PSO) is used to train the HC-MLP. A benchmark of fifty images is used to evaluate the performance of the algorithm and compare our proposal against statistical methods which use copula gaussian functions.

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“…Hernandez-Lopez and Rivera [7] adopt a change detection method to achieve real-time performance. This approach implements a probabilistic segmentation based on the quadratic Markov measure fields model.…”

mentioning

confidence: 99%