Tutorial 5 : Spatio‐temporal filtering for multi‐object tracking in image sequences
Prof. Andrea Cavallaro, Queen Mary University of London
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
Object detection and representation
Part C – Multi-target algorithms
Random Finite Sets for tracking
Tracking with context modelling
Birth and clutter intensity estimation
Part D - Performance evaluation
Analytical vs. empirical methods
Part E – Open problems and research outlook
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