Max-covering scheme for gesture recognition of Chinese traffic police

We present a method to recognize gestures made by Chinese traffic police in complex scenes based on a max-covering scheme for driver assistance systems and intelligent vehicles. Gesture recognition is made possible by upper-body-part detection with a five-part body model. First, the police’s torso and arms are extracted from a complex traffic scene as the foreground region by using dark channel prior and kernel density estimation. Then the coordinates of pixels in the upper arms and forearms are determined using the proposed max-covering scheme, which is based on a key observation that body-part tiles maximally cover the foreground region and satisfy a body plan. Finally, the rotation joint angle or Gabor feature-based two-dimensional principal component analysis is used to recognize the gestures made by Chinese traffic police. A comparative study is proposed with other human pose estimation methods, which demonstrates that better recognition results can be obtained using the proposed method on a number of video sequences.