Scalable Multi-camera Tracking in a Metropolis

Part of the Advances in Computer Vision and Pattern Recognition book series (ACVPR)


The majority of work in person re-identification is focused primarily on the matching process at an algorithmic level, from identifying reliable features to formulating effective classifiers and distance metrics in order to improve matching scores on established ‘closed-world’ benchmark datasets of limited scope and size. Very little work has explored the pragmatic and ultimately challenging question of how to engineer working systems that best leverage the strengths and tolerate the weaknesses of the current state of the art in re-identification techniques, and which are capable of scaling to ‘open-world’ operational requirements in a large urban environment. In this work, we present the design rationale, implementational considerations and quantitative evaluation of a retrospective forensic tool known as Multi-Camera Tracking (MCT). The MCT system was developed for re-identifying and back-tracking individuals within huge quantities of open-world CCTV video data sourced from a large distributed multi-camera network encompassing different public transport hubs in a metropolis. There are three key characteristics of MCT, associativity, capacity and accessibility, that underpin its scalability to spatially large, temporally diverse, highly crowded and topologically complex urban environments with transport links. We discuss a multitude of functional features that in combination address these characteristics. We consider computer vision techniques and machine learning algorithms, including relative feature ranking for inter-camera matching, global (crowd-level) and local (person-specific) space–time profiling, attribute re-ranking and machine-guided data mining using a ‘man-in-the-loop’ interactive paradigm. We also discuss implementational considerations designed to facilitate linear scalability to an aribitrary number of cameras by employing a distributed computing architecture. We conduct quantitative trials to illustrate the potential of the MCT system and its performance characteristics in coping with very large-scale open-world multi-camera data covering crowded transport hubs in a metropolis.


Camera View Correct Match Query Engine Candidate Match Search Iteration 



We thank Lukasz Zalewski, Tao Xiang, Robert Koger, Tim Hospedales, Ryan Layne, Chen Change Loy and Richard Howarth of Vision Semantics and Queen Mary University of London who contributed to this work; Colin Lewis, Gari Owen and Andrew Powell of the UK MOD SA(SD) who made this work possible; Zsolt Husz, Antony Waldock, Edward Campbell and Paul Zanelli of BAE Systems who collaborated on this work; and Toby Nortcliffe of the UK Home Office CAST who assisted in setting up the trial environment and data capture.


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Copyright information

© Springer-Verlag London 2014

Authors and Affiliations

  1. 1.Vision Semantics LtdLondonUK
  2. 2.Queen Mary University of LondonLondonUK

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