Data Association for Multi-Object Visual Tracking

Margrit Betke, Zheng Wu

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商品描述

In the human quest for scientific knowledge, empirical evidence is collected by visual perception. Tracking with computer vision takes on the important role to reveal complex patterns of motion that exist in the world we live in. Multi-object tracking algorithms provide new information on how groups and individual group members move through three-dimensional space. They enable us to study in depth the relationships between individuals in moving groups. These may be interactions of pedestrians on a crowded sidewalk, living cells under a microscope, or bats emerging in large numbers from a cave. Being able to track pedestrians is important for urban planning; analysis of cell interactions supports research on biomaterial design; and the study of bat and bird flight can guide the engineering of aircraft. We were inspired by this multitude of applications to consider the crucial component needed to advance a single-object tracking system to a multi-object tracking system-data association.

Data association in the most general sense is the process of matching information about newly observed objects with information that was previously observed about them. This information may be about their identities, positions, or trajectories. Algorithms for data association search for matches that optimize certain match criteria and are subject to physical conditions. They can therefore be formulated as solving a "constrained optimization problem"-the problem of optimizing an objective function of some variables in the presence of constraints on these variables. As such, data association methods have a strong mathematical grounding and are valuable general tools for computer vision researchers.

This book serves as a tutorial on data association methods, intended for both students and experts in computer vision. We describe the basic research problems, review the current state of the art, and present some recently developed approaches. The book covers multi-object tracking in two and three dimensions. We consider two imaging scenarios involving either single cameras or multiple cameras with overlapping fields of view, and requiring across-time and across-view data association methods. In addition to methods that match new measurements to already established tracks, we describe methods that match trajectory segments, also called tracklets. The book presents a principled application of data association to solve two interesting tasks: first, analyzing the movements of groups of free-flying animals and second, reconstructing the movements of groups of pedestrians. We conclude by discussing exciting directions for future research.

商品描述(中文翻譯)

在人類對科學知識的追求中,通過視覺感知收集實證證據。計算機視覺的跟踪在揭示我們所生活的世界中存在的複雜運動模式方面扮演著重要角色。多目標跟踪算法提供了關於群體和個體成員在三維空間中移動方式的新信息。它們使我們能夠深入研究移動群體中個體之間的關係。這些關係可能是擁擠人行道上的行人互動、顯微鏡下的活細胞互動,或者大量蝙蝠從洞穴中飛出的情況。能夠跟踪行人對城市規劃很重要;分析細胞互動支持生物材料設計研究;研究蝙蝠和鳥類飛行可以指導飛機工程。我們受到這種多樣的應用的啟發,考慮到將單目標跟踪系統推進到多目標跟踪系統所需的關鍵組件-數據關聯。

數據關聯在最一般的意義上是將有關新觀察對象的信息與先前觀察到的信息進行匹配的過程。這些信息可能涉及它們的身份、位置或軌跡。數據關聯算法尋找優化某些匹配標準並受物理條件限制的匹配,因此可以被形式化為解決“受限制優化問題”-在這些變量存在約束條件的情況下優化目標函數的問題。因此,數據關聯方法具有強大的數學基礎,是計算機視覺研究人員寶貴的通用工具。

本書旨在作為數據關聯方法的教程,面向計算機視覺的學生和專家。我們描述了基本的研究問題,回顧了當前的最新技術,並介紹了一些最近開發的方法。本書涵蓋了二維和三維的多目標跟踪。我們考慮了兩種成像場景,一種涉及單個攝像頭,另一種涉及具有重疊視野的多個攝像頭,並需要跨時間和跨視野的數據關聯方法。除了將新測量匹配到已建立的軌跡的方法外,我們還描述了將軌跡段匹配的方法,也稱為軌跡片段。本書提出了一種基於數據關聯的原則應用,以解決兩個有趣的任務:首先,分析自由飛行動物群體的運動,其次,重建行人群體的運動。最後,我們討論了未來研究的激動人心的方向。