Dynamic textures based target detection for PTZ camera sequences
M. Sami Zitouni, Harish Bhaskar, Andrzej Stefan Sluzek · 2017
In this paper, a temporally iterative Gaussian Mixture Model (GMM) of Dynamic Texture (DT) for target detection using a moving PTZ camera, is proposed. Camera movement in a PTZ sensor causes motion-based target detection techniques to fail for the periods affected by the scene change. This is because the whole scene is considered a representation of the target motion. When the camera is in motion, conventional background models remain invalid until the time that the model has adapted and updated its parameters to the newly perceived scene. The proposed model is based on an iterative modeling of spatio-temporal patches that represent the visual scene using GMM-of-DT. During the initial iteration of the proposed GMM-of-DT model, the input video is temporally segmented into clips in a manner that separates global from local motion. Further, parameters of the GMM-of-DT model are estimated for each temporal segment and in subsequent iterations updated adaptively to generate the final foreground mask. The proposed technique is tested and verified on video scenes from public datasets.