Given a video sequence acquired from a fixed camera, the stationary background generation problem consists of generating a unique image estimating the stationary background of the sequence. During the IEEE Scene Background Modeling Contest (SBMC) organized in 2016, we presented the LaBGen-P method. In short, this method relies on a motion detection algorithm for selecting, for each pixel location, a given amount of pixel intensities that are most likely static by keeping the ones with the smallest quantities of motion. These quantities are estimated by aggregating the motion scores returned by the motion detection algorithm in the spatial neighborhood of the pixel. After this selection process, the background image is then generated by blending the selected intensities with a median filter. In our previous works, we showed that using a temporally-memoryless motion detection, detecting motion between two frames without relying on additional temporal information, leads our method to achieve the best performance. In this work, we go one step further by developing LaBGen-P-Semantic, a variant of LaBGen-P, the motion detection step of which is built on the current frame only by using semantic segmentation. For this purpose, two intra-frame motion detection algorithms, detecting motion from a unique frame, are presented and compared. Our experiments, carried out on the Scene Background Initialization (SBI) and SceneBackgroundModeling.NET (SBMnet) datasets, show that leveraging semantic segmentation improves the robustness against intermittent motions, background motions and very short video sequences, which are among the main challenges in the background generation field. Moreover, our results confirm that using an intra-frame motion detection is an appropriate choice for our method and paves the way for more techniques based on semantic segmentation.
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