Motion-based object segmentation using hysteresis and bidirectional inter-frame change detection in sequences with moving camera

• Inter-frame change detection through bidirectional global motion compensated error.
• Automatic optimal weight selection for hysteresis weighted mean thresholding of error images.
• Temporal consistency using adaptive filtering.
• Evaluation using manually created ground truth has shown superior segmentation performance.
• Robustness under various H.264/AVC coding conditions.

We present an unsupervised motion-based object segmentation algorithm for video sequences with moving camera, employing bidirectional inter-frame change detection. For every frame, two error frames are generated using motion compensation. They are combined and a segmentation algorithm based on thresholding is applied. We employ a simple and effective error fusion scheme and consider spatial error localization in the thresholding step. We find the optimal weights for the weighted mean thresholding algorithm that enables unsupervised robust moving object segmentation. Further, a post processing step for improving the temporal consistency of the segmentation masks is incorporated and thus we achieve improved performance compared to the previously proposed methods. The experimental evaluation and comparison with other methods demonstrate the validity of the proposed method.