We propose a novel tracking algorithm that can work robustly in a challenging scenario such that several kinds of appearance and motion changes of an object occur at the same time. Our algorithm is based on a visual tracking decomposition

Figure 1. Unsupervised 3D point clouds generated by our tree-GAN for multiple classes (e.g., Motorbike, Laptop, Table, Guitar, Skateboard, Knife, Table, Pistol, and Car from top-left to bottom-right). Our tree-GAN can generate more accurate point clouds than baseline (i.e., r-GAN [1]), and can also produce point clouds for semantic parts of objects, which are denoted by different colors. AbstractIn this paper, we propose a novel generative adversarial network (GAN) for 3D point clouds generation, which is called tree-GAN. To achieve state-of-the-art performance for multi-class 3D point cloud generation, a tree-structured graph convolution network (TreeGCN) is introduced as a generator for tree-GAN. Because TreeGCN performs graph convolutions within a tree, it can use ancestor information to boost the representation power for features. To evaluate GANs for 3D point clouds accurately, we develop a novel evaluation metric called Fréchet point cloud distance (FPD). Experimental results demonstrate that the proposed tree-GAN outperforms state-of-the-art GANs in terms of * Authors contributed equally both conventional metrics and FPD, and can generate point clouds for different semantic parts without prior knowledge.

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In this paper, we propose a novel on-line visual tracking framework based on the Siamese matching network and meta-learner network, which run at real-time speeds. Conventional deep convolutional feature-based discriminative visual tracking algorithms require continuous re-training of classifiers or correlation filters, which involve solving complex optimization tasks to adapt to the new appearance of a target object. To alleviate this complex process, our proposed algorithm incorporates and utilizes a meta-learner network to provide the matching network with new appearance information of the target objects by adding targetaware feature space. The parameters for the target-specific feature space are provided instantly from a single forwardpass of the meta-learner network. By eliminating the necessity of continuously solving complex optimization tasks in the course of tracking, experimental results demonstrate that our algorithm performs at a real-time speed while maintaining competitive performance among other state-ofthe-art tracking algorithms.

We propose a novel tracking algorithm based on the Wang-Landau Monte Carlo (WLMC) sampling method for dealing with abrupt motions efficiently. Abrupt motions cause conventional tracking methods to fail because they violate the motion smoothness constraint. To address this problem, we introduce the Wang-Landau sampling method and integrate it into a Markov Chain Monte Carlo (MCMC)-based tracking framework. By employing the novel density-of-states term estimated by the Wang-Landau sampling method into the acceptance ratio of MCMC, our WLMC-based tracking method alleviates the motion smoothness constraint and robustly tracks the abrupt motions. Meanwhile, the marginal likelihood term of the acceptance ratio preserves the accuracy in tracking smooth motions. The method is then extended to obtain good performance in terms of scalability, even on a high-dimensional state space. Hence, it covers drastic changes in not only position but also scale of a target. To achieve this, we modify our method by combining it with the N-fold way algorithm and present the N-Fold Wang-Landau (NFWL)-based tracking method. The N-fold way algorithm helps estimate the density-of-states with a smaller number of samples. Experimental results demonstrate that our approach efficiently samples the states of the target, even in a whole state space, without loss of time, and tracks the target accurately and robustly when position and scale are changing severely.