Bayesian Surprise for Small and Sub-Pixel Moving Target Detection

Detecting very small moving targets (sub-pixel to a few pixels) has not benefited much from recent advances in deep neural networks, as these typically rely on texture features that are absent in very small targets. We present a new algorithm using Bayesian surprise. It computes how an artificial observer’s beliefs about the contents of a video sequence evolve over space and time. Surprise builds a distribution of beliefs for each pixel in the video. On every new video frame, Bayes’ theorem updates the prior belief distributions into posterior distributions. Surprise arises when/where these updates are significant, as measured by the Kullback-Leibler divergence. Surprise outperforms 27 baselines on downscaled versions of the public Neovision2-Tower dataset (50 videos, 45,000 frames total, 5 target types). Notably, surprise is more robust to wide-area perturbations (e.g., foliage fluttering in the wind, clouds passing by) than other pixel-level models.