Adaptive Exponential Smoothing for Online Filtering of Pixel Prediction Maps

We propose an efficient online video filtering method, called adaptive exponential filtering (AES) to refine pixel prediction maps. Assuming each pixel is associated with a discriminative prediction score, the proposed AES applies exponentially decreasing weights over time to smooth the prediction score of each pixel, similar to classic exponential smoothing. However, instead of fixing the spatial pixel location to perform temporal filtering, we trace each pixel in the past frames by finding the optimal path that can bring the maximum exponential smoothing score, thus performing adaptive and non-linear filtering. Thanks to the pixel tracing, AES can better address object movements and avoid over-smoothing. To enable real-time filtering, we propose a linear-complexity dynamic programming scheme that can trace all pixels simultaneously. We apply the proposed filtering method to improve both saliency detection maps and scene parsing maps. The comparisons with average and exponential filtering, as well as state-of-the-art methods, validate that our AES can effectively refine the pixel prediction maps, without using the original video again.