A diffusion framework for detection of moving vehicles

Schclar, Alon; Averbuch, Amir; Rabin, Neta; Zheludev, Valery A.; Hochman, K.

doi:10.1016/j.dsp.2009.02.002

Cited by 35 publications

(27 citation statements)

References 13 publications

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“…This constraint led to utilizing the newly introduced random projection method, as an efficient tool for feature extraction, which does not require heavy processing. In addition, this paper gives some comparison between the use of random projections as opposed to more traditional signal processing tools like wavelets, and more sophisticated dimensionality reduction methods like Diffusion Maps, which were used in [4,21].…”

Section: Structure Of the Dataset And Problem Descriptionmentioning

confidence: 99%

“…Another recent paper [4] distinguishes between vehicles and background by using wavelet packet coefficients with a procedure of random search for a near-optimal footprint. In [21], wavelet packet coefficients follwed by the application of Diffusion Maps [20], was used for vehicle classification. The "eigenfaces method" [22], which was originally used for human face recognition, to distinguish between different vehicle sound signatures, was used in [11].…”

Section: Related Workmentioning

confidence: 99%

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Dimensionality reduction for detection of moving vehicles

Averbuch

Rabin

Schclar

et al. 2011

Pattern Anal Applic

Self Cite

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Automatic acoustic-based vehicle detection is a common task in security and surveillance systems. Usually, a recording device is placed in a designated area and a hardware/software system processes the sounds that are intercepted by this recording device to identify vehicles only as they pass by. An algorithm, which is suitable for online automatic detection of vehicles, which is based on their online acoustic recordings, is proposed. The scheme uses dimensionality reduction methodologies such as random projections instead of using traditional signal processing methods to extract features. It uncovers characteristic features of the recorded sounds without any assumptions about the structure of the signal. The set of features is classified by the application of PCA. The microphone is opened all the time and the algorithm filtered out many background noises such as wind, steps, speech, airplanes, etc. The introduced algorithm is generic and can be applied to various signal types for solving different detection and classification problems.

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Section: Structure Of the Dataset And Problem Descriptionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Dimensionality reduction for detection of moving vehicles

Averbuch

Rabin

Schclar

et al. 2011

Pattern Anal Applic

Self Cite

View full text Add to dashboard Cite

show abstract

“…Choosing the parameter is not trivial. It should be large enough to cover the local neighborhood but small so that it does not cover too much of it [21].…”

Section: Dimensionality Reductionmentioning

confidence: 99%

“…Diffusion maps have been applied to various data mining problems. These include vehicle classification by sound [21], music tonality [10], sensor fusion [12], radio network problem detection [25] and detection of injection attacks [8]. Advantages of this approach are that the dimensionality of the data is reduced and that it can be used unsupervised [2].…”

Section: Introductionmentioning

confidence: 99%

Anomaly Detection from Network Logs Using Diffusion Maps

Sipola

Juvonen

Lehtonen

2011

Engineering Applications of Neural Networks

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Abstract. The goal of this study is to detect anomalous queries from network logs using a dimensionality reduction framework. The fequencies of 2-grams in queries are extracted to a feature matrix. Dimensionality reduction is done by applying diffusion maps. The method is adaptive and thus does not need training before analysis. We tested the method with data that includes normal and intrusive traffic to a web server. This approach finds all intrusions in the dataset.

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“…For example it was used to improve audio quality by suppressing transient interference [30]. In [26] it was utilized for detecting moving vehicles. Additionally, DM was proposed for scene classification [19], gene expression analysis [25] and source localization [29].…”

Section: Introductionmentioning

confidence: 99%

Approximately-isometric diffusion maps

Salhov

Bermanis

Wolf

et al. 2015

Applied and Computational Harmonic Analysis

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Diffusion Maps (DM), and other kernel methods, are utilized for the analysis of high dimensional datasets. The DM method uses a Markovian diffusion process to model and analyze data. A spectral analysis of the DM kernel yields a map of the data into a low dimensional space, where Euclidean distances between the mapped data points represent the diffusion distances between the corresponding high dimensional data points. Many machine learning methods, which are based on the Euclidean metric, can be applied to the mapped data points in order to take advantage of the diffusion relations between them. However, a significant drawback of the DM is the need to apply spectral decomposition to a kernel matrix, which becomes infeasible for large datasets. In this paper, we present an efficient approximation of the DM embedding. The presented approximation algorithm produces a dictionary of data points by identifying a small set of informative representatives. Then, based on this dictionary, the entire dataset is efficiently embedded into a low dimensional space. The Euclidean distances in the resulting embedded space approximate the diffusion distances. The properties of the presented embedding and its relation to DM method are analyzed and demonstrated.

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A diffusion framework for detection of moving vehicles

Cited by 35 publications

References 13 publications

Dimensionality reduction for detection of moving vehicles

Dimensionality reduction for detection of moving vehicles

Anomaly Detection from Network Logs Using Diffusion Maps

Approximately-isometric diffusion maps

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