Multiple target tracking for wearable and robotic cameras

Supervisor
Andrea Cavallaro - Queen Mary University of London
Date and time
Friday, May 5, 2017 at 11:00 AM - Sala Verde - Rinfresco 10.45, inizio seminario 11.00.
Place
Ca' Vignal - Piramide, Floor 0, Hall Verde
Programme Director
External reference
Publication date
April 18, 2017
Department
Computer Science  

Summary

Vision offers a powerful sensing modality to understand and interact with the physical world.  The rapid progress in hardware, models and algorithms is supporting the emergence of applications for the recognition of events from wearable smart cameras and camera-equipped robots, such as unmanned land and aerial vehicles (i.e. self-driving cars and mini-drones).
In this context I will present an online multi-target tracker that exploits both high- and low-confidence target detections in a Probability Hypothesis Density Particle Filter framework to continuously localise people from moving cameras. High-confidence detections are used for label propagation and target initialization, whereas low-confidence detections only support the propagation of labels. Data association is performed after prediction to avoid computationally expensive labelling procedures such as clustering. I will discuss results on the Multiple Object Tracking benchmark dataset and present several application scenarios.

Contact person: Gloria Menegaz





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