Fast event-driven incremental learning of hand symbols

Abstract

This paper describes a hand symbol recognition system that can quickly be trained to incrementally learn to recognize new symbols using about 100 times less data and time than by using conventional training. It is driven by frames from a Dynamic Vision Sensor (DVS) event camera. Conventional cameras have very redundant output, especially at high frame rates. Dynamic vision sensors output sparse and asynchronous brightness change events that occur when an object or the camera is moving. Images consisting of a fixed number of events from a DVS drive recognition and incremental learning of new hand symbols in the context of a RoShamBo (rock-paper-scissors) demonstration. Conventional training on the original RoShamBo dataset requires about 12.5h compute time on a desktop GPU using the 2.5 million images in the base dataset. Novel symbols that a user shows for a few tens of seconds to the system can be learned on-the-fly using the iCaRL incremental learning algorithm with 3 minutes of training time on a desktop GPU, while preserving recognition accuracy of previously trained symbols. Our system runs a residual network with 32 layers and maintains 88.4% after 100 epochs or 77% after 5 epochs overall accuracy after 4 incremental training stages. Each stage adds an additional 2 novel symbols to the base 4 symbols. The paper also reports an inexpensive robot hand used for live demonstrations of the base RoShamBo game.