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Published May 6, 2014 | Submitted
Conference Paper Open

From Categories to Individuals in Real Time — A Unified Boosting Approach

Abstract

A method for online, real-time learning of individual-object detectors is presented. Starting with a pre-trained boosted category detector, an individual-object detector is trained with near-zero computational cost. The individual detector is obtained by using the same feature cascade as the category detector along with elementary manipulations of the thresholds of the weak classifiers. This is ideal for online operation on a video stream or for interactive learning. Applications addressed by this technique are reidentification and individual tracking. Experiments on four challenging pedestrian and face datasets indicate that it is indeed possible to learn identity classifiers in real-time; besides being faster-trained, our classifier has better detection rates than previous methods on two of the datasets.

Additional Information

This work is funded by the ARO/JPL-NASA Stennis grant NAS7.03001 and the ONR MURI Grant N00014-10-1-0933.

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Additional details

Created:
August 19, 2023
Modified:
October 20, 2023