Survey on Faster Region Convolution Neural Network for Object Detection

Convolution Neural Networks uses the concepts of deep learning and becomes the golden standard for image classification. This algorithm was implemented even in complicated sights with multiple overlapping objects, different backgrounds and it also successfully identified and classified objects along with their boundaries, differences and relations to one another. Then comes Region-based Convolutional Neural Networks(R-CNN)which is further more described into two types that is Fast R-CNN and Faster R-CNN. This R-CNN method is to use selective search to extract only 2000 regions from the image and cannot be implemented in real time as it would take 47 sec approximately for each test image. Then comes the fast R-CNN in which changes are made to overcome the drawbacks in R-CNN algorithm in which the 2000 region proposals are not fed to the CNN instead the image is fed directly to the CNN to generate Convolutional feature map. This was then replaced by faster R-CNN which came up with an object detection algorithm that eliminates the selective search algorithm to perform the operation. This algorithm takes 0.2 sec approximately for the test image and we will be using this for real time object detection.So, basically in this paper we are doing research on Faster R-CNN that is being used for object detection method

Spatial-Temporal Feature Extraction and Evaluation Network for Citywide Traffic Condition Prediction

Traffic prediction plays an important role in the realization of traffic control and scheduling tasks in intelligent transportation systems. With the diversification of data sources, reasonably using rich traffic data to model the complex spatial-temporal dependence and nonlinear characteristics in traffic flow are the key challenge for intelligent transportation system. In addition, clearly evaluating the importance of spatial-temporal features extracted from different data becomes a challenge. A Double Layer - Spatial Temporal Feature Extraction and Evaluation (DL-STFEE) model is proposed. The lower layer of DL-STFEE is spatial-temporal feature extraction layer. The spatial and temporal features in traffic data are extracted by multi-graph graph convolution and attention mechanism, and different combinations of spatial and temporal features are generated. The upper layer of DL-STFEE is the spatial-temporal feature evaluation layer. Through the attention score matrix generated by the high-dimensional self-attention mechanism, the spatial-temporal features combinations are fused and evaluated, so as to get the impact of different combinations on prediction effect. Three sets of experiments are performed on actual traffic datasets to show that DL-STFEE can effectively capture the spatial-temporal features and evaluate the importance of different spatial-temporal feature combinations.Comment: 39 pages, 14 figures, 5 table

Future Transportation

Greenhouse gas (GHG) emissions associated with transportation activities account for approximately 20 percent of all carbon dioxide (co2) emissions globally, making the transportation sector a major contributor to the current global warming. This book focuses on the latest advances in technologies aiming at the sustainable future transportation of people and goods. A reduction in burning fossil fuel and technological transitions are the main approaches toward sustainable future transportation. Particular attention is given to automobile technological transitions, bike sharing systems, supply chain digitalization, and transport performance monitoring and optimization, among others