Robot autonomous navigation

Autonomous vehicle navigation is a very popular research area in the vision and control field. Based on Prof. Dickmanns' philosophy, we implement a navigation algorithm on thc small robot. The robot can rely on its eyes (the camera mounted on thc top of the robot) and control its wheels to walk through the sub-basement hallways of Caltech Moore Lab building. The speed we achieve is robot's mechanical maximum speed 0.5 m/s

Detecting Simultaneous Integer Relations for Several Real Vectors

An algorithm which either finds an nonzero integer vector ${\mathbf m}$ for given $t$ real $n$-dimensional vectors ${\mathbf x}_1,...,{\mathbf x}_t$ such that ${\mathbf x}_i^T{\mathbf m}=0$ or proves that no such integer vector with norm less than a given bound exists is presented in this paper. The cost of the algorithm is at most ${\mathcal O}(n^4 + n^3 \log \lambda(X))$ exact arithmetic operations in dimension $n$ and the least Euclidean norm $\lambda(X)$ of such integer vectors. It matches the best complexity upper bound known for this problem. Experimental data show that the algorithm is better than an already existing algorithm in the literature. In application, the algorithm is used to get a complete method for finding the minimal polynomial of an unknown complex algebraic number from its approximation, which runs even faster than the corresponding \emph{Maple} built-in function.Comment: 10 page

Generalized Batch Normalization: Towards Accelerating Deep Neural Networks

Utilizing recently introduced concepts from statistics and quantitative risk management, we present a general variant of Batch Normalization (BN) that offers accelerated convergence of Neural Network training compared to conventional BN. In general, we show that mean and standard deviation are not always the most appropriate choice for the centering and scaling procedure within the BN transformation, particularly if ReLU follows the normalization step. We present a Generalized Batch Normalization (GBN) transformation, which can utilize a variety of alternative deviation measures for scaling and statistics for centering, choices which naturally arise from the theory of generalized deviation measures and risk theory in general. When used in conjunction with the ReLU non-linearity, the underlying risk theory suggests natural, arguably optimal choices for the deviation measure and statistic. Utilizing the suggested deviation measure and statistic, we show experimentally that training is accelerated more so than with conventional BN, often with improved error rate as well. Overall, we propose a more flexible BN transformation supported by a complimentary theoretical framework that can potentially guide design choices.Comment: accepted at AAAI-1

Analysis of Mobile Game Intellectual Property Marketing Strategy: A Case Study of Honor of Kings

The mobile game market is now gradually forming a unique ecological industry chain. Game parties are beginning to look beyond the game experience and focus on building mature game IPs. By creating specific IP to drive the game’s peripheral revenue, strengthen the emotional connection with game users, and achieve the purpose of long-term development. Looking at the domestic market, Honor of Kings, as a phenomenal mobile game in China, its IP development and marketing are of reference learning significance. In this paper, we selected Honor of Kings as the research object, and we collected data through both questionnaire surveys and interviews, using SPSS for statistical analysis. The research analyzed its IP marketing strategy and effect and searched for the factors which affect its IP marketing effect. It finds that the impact of Honor of Kings IP marketing is influenced by the degree of perfection of Honor of Kings worldview, i.e., IP connotation and local cultural awareness. At the same time, we analyzed the IP development process and marketing strategy of Honor of Kings in combination, pointed out its advantages and shortcomings, and gave suggestions to provide new ideas for IP marketing of other game companies

Towards Detection of Human Motion

Detecting humans in images is a useful application of computer vision. Loose and textured clothing, occlusion and scene clutter make it a difficult problem because bottom-up segmentation and grouping do not always work. We address the problem of detecting humans from their motion pattern in monocular image sequences; extraneous motions and occlusion may be present. We assume that we may not rely on segmentation, nor grouping and that the vision front-end is limited to observing the motion of key points and textured patches in between pairs of frames. We do not assume that we are able to track features for more than two frames. Our method is based on learning an approximate probabilistic model of the joint position and velocity of different body features. Detection is performed by hypothesis testing on the maximum a posteriori estimate of the pose and motion of the body. Our experiments on a dozen of walking sequences indicate that our algorithm is accurate and efficient

3DCFS : Fast and robust joint 3D semantic-instance segmentation via coupled feature selection

We propose a novel fast and robust 3D point clouds segmentation framework via coupled feature selection, named 3DCFS, that jointly performs semantic and instance segmentation. Inspired by the human scene perception process, we design a novel coupled feature selection module, named CFSM, that adaptively selects and fuses the reciprocal semantic and instance features from two tasks in a coupled manner. To further boost the performance of the instance segmentation task in our 3DCFS, we investigate a loss function that helps the model learn to balance the magnitudes of the output embedding dimensions during training, which makes calculating the Euclidean distance more reliable and enhances the generalizability of the model. Extensive experiments demonstrate that our 3DCFS outperforms state-of-the-art methods on benchmark datasets in terms of accuracy, speed and computational cost