TRINet: Tracking and Re-identification Network for Multiple Targets in Egocentric Videos Using LSTMs

Abstract

We present a recurrent network based novel framework for tracking and re-identifying multiple targets in first-person perspective. Even though LSTMs can act as a sequence classifier, most of the previous works in multi target tracking use their output with some distance metric for data association. In this work, we employ an LSTM as a classifier and train it over the memory cells output vectors corresponding to different targets obtained from another LSTM. This classifier, based on appearance and motion features, discriminates the targets in two consecutive frames as well as re-identify them in a time interval. We integrate this classifier as an additional block in a detection free tracking architecture which enhances the performance in terms of re-identification of targets and also indicates the absence of targets. We propose a dataset of twenty egocentric videos containing multiple targets to validate our approach.