Change-point Detection Methods for Body-Worn Video

Allen, Stephanie; Madras, David; Ye, Ye; Zanotti, Greg

doi:10.48550/arxiv.1610.06453

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2018

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“…We refer to [32] for a more thorough review. In recent applications, the need arises to perform such methods on embedded devices, for instance in video streams from body-worn or surveillance video cameras [2], or on data collected by smart phones [22]. In addition to being constrained by limited power and memory, such personal devices collect data that can be potentially sensitive.…”

Section: Introductionmentioning

confidence: 99%

NEWMA: a new method for scalable model-free online change-point detection

Keriven¹,

Garreau²,

Poli³

2018

Preprint

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We consider the problem of detecting abrupt changes in the distribution of a multidimensional time series, with limited computing power and memory. In this paper, we propose a new method for model-free online change-point detection that relies only on fast and light recursive statistics, inspired by the classical Exponential Weighted Moving Average algorithm (EWMA). The proposed idea is to compute two EWMA statistics on the stream of data with different forgetting factors, and to compare them. By doing so, we show that we implicitly compare recent samples with older ones, without the need to explicitly store them. Additionally, we leverage Random Features to efficiently use the Maximum Mean Discrepancy as a distance between distributions. We show that our method is orders of magnitude faster than usual non-parametric methods for a given accuracy.

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Section: Introductionmentioning

confidence: 99%