Abstract: There is a primal challenge to eliminate local edges from noisy clutter while simultaneously preserving the complete object silhouette in dynamic contour detection. Inspired by biological evidences for visual motion perception, we formulate the motion energy inhibition model as a computational mechanism for effective background suppression and foreground enhancement in boundary responses. The normalized integration with four-direction-channel motion-filter response in spatio-temporal slices reflects the dynamical "surrounding-suppression" characteristic in V1 visual neuron, which uses two half-disc structure to extract contour gradient. Finally, we exploit the random forest model to partition the contour gradient from jointly motion and appearance cues in tree-like style to achieve object contours in video. Experimental results demonstrate better performances of this approach in quantitative precision-recall curve, F-measure and AP values, and qualitative visual effects.