Using Stacked Auto-Encoder To Get Feature With Continuity And Distinguishability In Multi-Object Tracking

Good feature expression of targets plays an important role in multi-object tracking (MOT). Inspired by the self-learning concept of deep learning methods, an online feature extraction scheme is proposed in this paper, based on a conditional random field (CRF). The CRF model is transformed into a certain number of multi-scale stacked auto-encoders with a new loss function. Features obtained with our method contain both continuous and distinguishable characteristics of targets. The inheritance relationship of stacked auto-encoders between adjacent frames is implemented by an online process. Features extracted from our online scheme are applied to improve the network flow tracking model. Experiment results show that the features by our method achieve better performance compared with other handcrafted-features. The overall tracking performance are improved when our features are used in the MOT tasks.