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Tutorial 5 : Spatio‐temporal filtering for multi‐object tracking in image sequences


Prof. Andrea Cavallaro, Queen Mary University of London
Dr. Emilio Maggio, Vicon, UK


This proposed ICIP 2011 tutorial will cover the fundamental aspects of spatio-temporal filtering for multiobject tracking in image sequences. The tutorial sets forth the state-of-the-art in object detection and representation, data association and random finite sets for video tracking. The tutorial will discuss and demonstrate the latest multi-target tracking algorithms with a unified and comprehensive coverage.

Using practical examples and illustration as support, we will introduce the participants in a discussion of the advantages and the limitations of traditional and modern approaches and we will guide them toward more efficient and accurate multi-target tracking algorithms, in particular those based on the Probabilistic Hypothesis Density Filter, a recent advance in image-based tracking that allows to reduce the computation complexity for multiple target tracking as well as to incorporate contextual knowledge to improve the efficiency and robustness of the filter.

Multiple-target tracking applications will be discussed in real-world tracking scenarios. We will conclude the tutorial by discussing tracking evaluation methodologies and introducing a collection of software resources and publicly available datasets to help the attendees develop and test multi-object trackers.


The tutorial material will be organized according to the proposed syllabus below. The tutorial slides will be provided as PDF for inclusion in the course distribution material. A website will be developed for the course, which will contain links to supporting material and video segments that enrich the learning experience of the participants.

Part 1 - Introduction Tracking in image sequences: definitions and problem formulation Applications Multi-target management: challenges

Part B Background Introduction to Bayesian tracking

  • Dynamic and observation models
  • Kalman filter
  • Monte Carlo approximation

Object detection and representation

Part C Multi-target algorithms Measurement validation
Data association

  • Nearest Neighbour
  • Graph matching
  • Multiple Hypothesis Tracking

Random Finite Sets for tracking
Probabilistic Hypothesis Density filter
The Particle PHD filter

  • Dynamic and observation models
  • Birth and clutter models
  • Importance sampling and resampling

Tracking with context modelling

  • Contextual information
  • ''Influence of the context on the filter

Birth and clutter intensity estimation
Tracking with contextual feedback

Part D - Performance evaluation Analytical vs. empirical methods
Evaluation scores, protocols and datasets
Comparing multi-target trackers

  • Target life-span
  • Statistical significance
  • Repeatability

Part E Open problems and research outlook

2011 UCL/TELE || icip2011-webmaster@listes.uclouvain.be || Last updated September 06, 2010, at 03:01 PM