Stereo-based human head detection from crowd scenes

In this paper, a novel stereo-based head detection method is proposed for human detection in crowd scene. It contains three steps: (1) scale-adaptive filtering, (2) spurious clue suppression and (3) human head location. With the depth information, the sizes of human heads could be estimated. From this, 3D scale-adaptive filtering is proposed. It is applied for extracting the likelihood evidence of heads from the stereo image. In the second step, the extracted points whose positions in the real space are much higher or lower than the average human height above the ground surface are further suppressed. Finally, human heads are located by applying a mean-shift algorithm to the likelihood map. Good results of detecting human heads in crowds have been obtained from the experiments on real scene.