Enhancing real-time human detection based on histograms of oriented gradients

In this paper we propose a human detection framework based on an enhanced version of Histogram of Oriented Gradients (HOG) features. These feature descriptors are computed with the help of a precalculated histogram of square-blocks. This novel method outperforms the integral of oriented histograms allowing the calculation of a single feature four times faster. Using Adaboost for HOG feature selection and Support Vector Machine as weak classifier, we build up a real-time human classifier with an excellent detection rate.Peer Reviewe

Globally Guided Trajectory Planning in Dynamic Environments

Navigating mobile robots through environments shared with humans is challenging. From the perspective of the robot, humans are dynamic obstacles that must be avoided. These obstacles make the collision-free space nonconvex, which leads to two distinct passing behaviors per obstacle (passing left or right). For local planners, such as receding-horizon trajectory optimization, each behavior presents a local optimum in which the planner can get stuck. This may result in slow or unsafe motion even when a better plan exists. In this work, we identify trajectories for multiple locally optimal driving behaviors, by considering their topology. This identification is made consistent over successive iterations by propagating the topology information. The most suitable high-level trajectory guides a local optimization-based planner, resulting in fast and safe motion plans. We validate the proposed planner on a mobile robot in simulation and real-world experiments.Comment: 7 pages, 6 figures, accepted to IEEE International Conference on Robotics and Automation (ICRA) 202

General-Sum Multi-Agent Continuous Inverse Optimal Control

IEEE Modelling possible future outcomes of robot-human interactions is of importance in the intelligent vehicle and mobile robotics domains. Knowing the reward function that explains the observed behaviour of a human agent is advantageous for modelling the behaviour with Markov Decision Processes (MDPs). However, learning the rewards that determine the observed actions from data is complicated by interactions. We present a novel inverse reinforcement learning(IRL) algorithm that can infer the reward function in multi-agent interactive scenarios. In particular, the agents may act boundedly rational (i.e., sub-optimal), a characteristic that is typical for human decision making. Additionally, every agent optimizes its own reward function which makes it possible to address non-cooperative setups. In contrast to other methods, the algorithm does not rely on reinforcement learning during inference of the parameters of the reward function. We demonstrate that our proposed method accurately infers the ground truth reward function in two-agent interactive experiments

Factorizing numbers with classical interference: several implementations in optics

Truncated Fourier, Gauss, Kummer and exponential sums can be used to factorize numbers: for a factor these sums equal unity in absolute value, whereas they nearly vanish for any other number. We show how this factorization algorithm can emerge from superpositions of classical light waves and we present a number of simple implementations in optics

Fast Road Sign Detection Using Hough Transform for Assisted Driving of Road Vehicles

Abstract. A system for real-time traffic sign detection is described in this paper. The system uses restricted Hough transform for circumferences in order to detect circular signs, and for straight lines for triangular ones. Some results obtained from a set of real road images captured under both normal and adverse weather conditions are presented as well in order to illustrate the robustness of the detection system. The average processing time is 30 ms per frame, what makes the system a good approach to work in real time conditions.

Lamb Shift of 3P and 4P states and the determination of α\alpha

The fine structure interval of P states in hydrogenlike systems can be determined theoretically with high precision, because the energy levels of P states are only slightly influenced by the structure of the nucleus. Therefore a measurement of the fine structure may serve as an excellent test of QED in bound systems or alternatively as a means of determining the fine structure constant $\alpha$ with very high precision. In this paper an improved analytic calculation of higher-order binding corrections to the one-loop self energy of 3P and 4P states in hydrogen-like systems with low nuclear charge number $Z$ is presented. A comparison of the analytic results to the extrapolated numerical data for high $Z$ ions serves as an independent test of the analytic evaluation. New theoretical values for the Lamb shift of the P states and for the fine structure splittings are given.Comment: 33 pages, LaTeX, 4 tables, 4 figure