Tracking using dynamic programming for appearance-based sign language recognition

TL;DR: A novel tracking algorithm that uses dynamic programming to determine the path of target objects and that is able to track an arbitrary number of different objects and can analogously be integrated into a hidden Markov model based recognition process.

Abstract: We present a novel tracking algorithm that uses dynamic programming to determine the path of target objects and that is able to track an arbitrary number of different objects. The traceback method used to track the targets avoids taking possibly wrong local decisions and thus reconstructs the best tracking paths using the whole observation sequence. The tracking method can be compared to the nonlinear time alignment in automatic speech recognition (ASR) and it can analogously be integrated into a hidden Markov model based recognition process. We show how the method can be applied to the tracking of hands and the face for automatic sign language recognition.