  Vision-based automatic counting of people has widespread applications in
intelligent transportation systems, security, and logistics. However, there is
currently no large-scale public dataset for benchmarking approaches on this
problem. This work fills this gap by introducing the first real-world RGB-D
People Counting DataSet (PCDS) containing over 4,500 videos recorded at the
entrance doors of buses in normal and cluttered conditions. It also proposes an
efficient method for counting people in real-world cluttered scenes related to
public transportations using depth videos. The proposed method computes a point
cloud from the depth video frame and re-projects it onto the ground plane to
normalize the depth information. The resulting depth image is analyzed for
identifying potential human heads. The human head proposals are meticulously
refined using a 3D human model. The proposals in each frame of the continuous
video stream are tracked to trace their trajectories. The trajectories are
again refined to ascertain reliable counting. People are eventually counted by
accumulating the head trajectories leaving the scene. To enable effective head
and trajectory identification, we also propose two different compound features.
A thorough evaluation on PCDS demonstrates that our technique is able to count
people in cluttered scenes with high accuracy at 45 fps on a 1.7 GHz processor,
and hence it can be deployed for effective real-time people counting for
intelligent transportation systems.
