Pixel Club: Coresets for Kinematic Data: From Theorems to Autonomous Toy-Drones

Dan Feldman (Haifa University)
Tuesday, 4.4.2017, 11:00
EE Meyer Building 1061

A coreset (or core-set) of a dataset is its semantic compression with respect to a set of queries, such that querying the (small) coreset provably yields an approximate answer to querying the original (full) dataset. However, we are not aware of real-time systems that compute coresets in a rate of dozens of frames per second. I will suggest a framework to turn theorems to such systems using coresets. This is by maintaining such a coreset for kinematic (moving) set of n points, and run algorithms on the small coresets, instead of the n points, in real time using weak devices. This also enabled my group to implement a low-cost (< $100) mini-computer with a wireless system that tracks a toy (and harmless) quadcopter which guides guests to a desired room in our department with no help of additional human or remote controller. I will present the system, the sketch of the proofs, as well as extensive experimental results.

* A joint work with Soliman Nasser and Ibrahim Jubran

Short Bio:
Dan Feldman is a faculty member and the head of the new Robotics & Big Data Labs in the University of Haifa, after returning from a 3 years post-doc at the robotics lab of MIT. During his PhD in the University of Tel-Aviv he developed data reduction techniques known as core-sets, based on computational geometry. Since his post-docs at Caltech and MIT, Dan's coresets are applied for main problems in Machine Learning, Big Data, computer vision, EEG and robotics. His group in Haifa continues to design and implement core-sets with provable guarantees for such real-time

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