Complete moving object detection in the context of robust subspace learning

Complete moving object detection plays a vital role in many applications of computer vision. For instance, depth estimation, scene understanding, object interaction, semantic segmentation, accident detection and avoidance in case of moving vehicles on a highway. However, it becomes challenging in the presence of dynamic backgrounds, camouflage, bootstrapping, varying illumination conditions, and noise. Over the past decade, robust subspace learning based methods addressed the moving objects detection problem with excellent performance. However, the moving objects detected by these methods are incomplete, unable to generate the occluded parts. Indeed, complete or occlusion-free moving object detection is still challenging for these methods. In the current work, we address this challenge by proposing a conditional Generative Adversarial Network (cGAN) conditioned on non-occluded moving object pixels during training. It therefore learns the subspace spanned by the moving objects covering all the dynamic variations and semantic information. While testing, our proposed Complete cGAN (CcGAN) is able to generate complete occlusion free moving objects in challenging conditions. The experimental evaluations of our proposed method are performed on SABS benchmark dataset and compared with 14 state-of-the-art methods, including both robust subspace and deep learning based methods. Our experiments demonstrate the superiority of our proposed model over both types of existing methods.