Two maximum entropy based algorithms for running quantile estimation in non-stationary data streams

TL;DR

This paper introduces two novel maximum entropy algorithms for accurately estimating running quantiles in non-stationary data streams with limited memory, outperforming existing methods significantly.

Contribution

The paper presents a new principle for efficient memory utilization and two algorithms that effectively estimate quantiles in non-stationary streams, surpassing prior approaches.

Findings

Both proposed algorithms outperform existing methods on synthetic and real data.

The data-aligned histogram algorithm achieves over 10 times lower error.

The algorithms work effectively with significantly less memory.

Abstract

The need to estimate a particular quantile of a distribution is an important problem which frequently arises in many computer vision and signal processing applications. For example, our work was motivated by the requirements of many semi-automatic surveillance analytics systems which detect abnormalities in close-circuit television (CCTV) footage using statistical models of low-level motion features. In this paper we specifically address the problem of estimating the running quantile of a data stream when the memory for storing observations is limited. We make several major contributions: (i) we highlight the limitations of approaches previously described in the literature which make them unsuitable for non-stationary streams, (ii) we describe a novel principle for the utilization of the available storage space, (iii) we introduce two novel algorithms which exploit the proposed principle in different ways, and (iv) we present a comprehensive evaluation and analysis of the proposed algorithms and the existing methods in the literature on both synthetic data sets and three large real-world streams acquired in the course of operation of an existing commercial surveillance system. Our findings convincingly demonstrate that both of the proposed methods are highly successful and vastly outperform the existing alternatives. We show that the better of the two algorithms (data-aligned histogram) exhibits far superior performance in comparison with the previously described methods, achieving more than 10 times lower estimate errors on real-world data, even when its available working memory is an order of magnitude smaller.