Online object tracking based on BLSTM-RNN with contextual-sequential labeling

Abstract

Object context has been verified its significance for appearance modeling in different proposed tracking-by-detection approaches. Unfortunately, the restrictive representation of the target’s contextual relationship within spatial domain has intensively limited its utility with high-level classification strategies. By investigating the learning capability of long-term dependencies from sequential data, in this paper, we propose a novel appearance model by transforming the target contextual dependency into a semantic sequential representation. It can be effectively utilized by a recurrent neural network embedded with bidirectional long short-term memory cells for online tracking-by-learning. Based on the trained BLSTM-RNN model, a searching mechanism by labeling score is proposed to improve the tracking robustness. With the implied appearance variation by labeling, the proposed tracking method has demonstrated to outperform most of state-of-the-art trackers on challenging benchmark videos via a heuristic strategy for model updating.