Abstract

We propose a novel Hierarchical Feature Model (HFM) for multi-target tracking. The traditional tracking algorithms use handcrafted features that cannot track targets accurately when the target model changes due to articulation, illumination intensity variation or perspective distortions. Our HFM explore deep features to model the appearance of targets. Then, we use an unsupervised dimensionality reduction for sparse representation of the feature vectors to cope with the time-critical nature of the tracking problem. Subsequently, a Bayesian filter is adopted as the tracker and a discrete combinatorial optimization is considered for target association. We compare our proposed HFM against 4 state-of-the-art trackers using 4 benchmark datasets. The experimental results show that our HFM outperforms all the state-of-the-art methods in terms of both Multi Object Tracking Accuracy (MOTA) and Multi Object Tracking Precision (MOTP).