Boosted Vehicle Detection Using Local and Global Features

This study presents a boosted vehicle detection system. It first hypothesizes potential locations of vehicles to reduce the computational costs by a statistic of the edge intensity and symmetry, then verifies the accuracy of the hypotheses using AdaBoost and Probabilistic Decision-Based Neural Network (PDBNN) classifiers, which exploit local and global features of vehicles, respectively. The combination of 2 classifiers can be used to learn the complementary relationship between local and global features, and it gains an extremely low false positive rate while maintaining a high detection rate. For the MIT Center for Biological & Computational Learning (CBCL) database, a 96.3% detection rate leads to a false alarm rate of approximately 0.0013%. The objective of this study is to extract the characteristic of vehicles in both local- and global-orientation, and model the implicit invariance of vehicles. This boosted approach provides a more effective solution to handle the problems encountered by conventional background-based detection systems. The experimental results of this study prove that the proposed system achieves good performance in detecting vehicles without background information. The implemented system also extract useful traffic information that can be used for further processing, such as tracking, counting, classification, and recognition.

Quantum topology identification with deep neural networks and quantum walks

Topologically ordered materials may serve as a platform for new quantum technologies,such as fault-tolerant quantum computers.To fulfil this promise,efficient and general methods are needed to discover and classify new topological phases of matter.We demonstrate that deep neural networks augmented with external memory can use the density profiles formed in quantum walks to efficiently identify properties of a topological phase as well as phase transitions.On a trial topological ordered model,our method’s accuracy of topological phase identification reaches 97.4%,and is shown to be robust to noise on the data.Furthermore,we demonstrate that our trained DNN is able to identify topological phases of a perturbed model,and predict the corresponding shift of topological phase transitions without learning any information about the perturbations in advance.These results demonstrate that our approach is generally applicable and may be used to identify a variety of quantum topological materials.