Improved human head and shoulder detection with local main gradient and tracklets-based feature

In this paper, a new approach is proposed which extracts local main gradients and tracklet-based features for describing human head-and-shoulders. Firstly, local main gradient is extracted for each sliding window such that only gradient features fitting a reasonable orientation are detected as candidate head-and-shoulders. Secondly, given that the shape of head-and-shoulder satisfies a specific curve, we model head-and-shoulder shapes as the combination of short trajectories (tracklets) and utilize the statistics of tracklets to describe head-and-shoulder shapes. Experiments show that by the introduction of our new features, we can achieve better detection results than existing head-and-shoulder detection methods.