Visual Tracking Using Wang–Landau Reinforcement Sampler

In this study, we present a novel tracking system, in which the tracking accuracy can be considerably enhanced by state prediction. Accordingly, we present a new Q-learning-based reinforcement method, augmented by Wang–Landau sampling. In the proposed method, reinforcement learning is used to predict a target configuration for the subsequent frame, while Wang–Landau sampler balances the exploitation and exploration degrees of the prediction. Our method can adapt to control the randomness of policy, using statistics on the number of visits in a particular state. Thus, our method considerably enhances conventional Q-learning algorithm performance, which also enhances visual tracking performance. Numerical results demonstrate that our method substantially outperforms other state-of-the-art visual trackers and runs in realtime because our method contains no complicated deep neural network architectures

Dual Gradient Based Snow Attentive Desnowing

In this paper, we propose a novel dual gradient-based desnowing algorithm that can accurately remove snow from a scene by characterizing snow particles. To localize snow in an image, we present a gradient-based snow activation map that can be estimated using snow classification. To recognize various patterns in the shapes and trajectories of snow particles, we introduce a gradient-based snow edge map. Using these two gradients, we estimate an accurate snow attention mask that is subsequently used for snow removal. In addition, we propose a translucency-aware context restoration network to handle various degrees of snow transparency and thus, prevent our method from losing the image context information during desnowing. Experimental results demonstrate that the proposed method considerably outperforms other state-of-the-art desnowing algorithms quantitatively and qualitatively