Object Detection based on Region Decomposition and Assembly

Region-based object detection infers object regions for one or more categories in an image. Due to the recent advances in deep learning and region proposal methods, object detectors based on convolutional neural networks (CNNs) have been flourishing and provided the promising detection results. However, the detection accuracy is degraded often because of the low discriminability of object CNN features caused by occlusions and inaccurate region proposals. In this paper, we therefore propose a region decomposition and assembly detector (R-DAD) for more accurate object detection. In the proposed R-DAD, we first decompose an object region into multiple small regions. To capture an entire appearance and part details of the object jointly, we extract CNN features within the whole object region and decomposed regions. We then learn the semantic relations between the object and its parts by combining the multi-region features stage by stage with region assembly blocks, and use the combined and high-level semantic features for the object classification and localization. In addition, for more accurate region proposals, we propose a multi-scale proposal layer that can generate object proposals of various scales. We integrate the R-DAD into several feature extractors, and prove the distinct performance improvement on PASCAL07/12 and MSCOCO18 compared to the recent convolutional detectors.Comment: Accepted to 2019 AAAI Conference on Artificial Intelligence (AAAI

A realistic simulation for self-organizing traffic lights

Traffic density has been growing during the last decades. New and better traffic light controllers are needed. Carlos Gershenson has proposed self- organizing traffic light controllers which are much better than current "green wave" methods. This has been tested by simulation with a realistic traffic simulator, which is an extended version of the Green Light District / iAtracos project. The simulations of the traffic light controllers are done for three scenarios. The third scenario is the Wetstraat of Brussels, which is created to approach the real infrastructure and traffic dynamics. The simulation results show that the proposed Sotl-platoon controller is much better than the green wave controller

Analysis of non-stationary spatial data : A study on the performances of Universal Kriging, Median-Polish Kriging and LOESS

One of major problems in spatial analysis is to estimate the value z(s(0)) at an unknown location s(0) using the information about observations z(s(α)), α = 1,…,n. In this article, we will perform a numerical study about some methods for this problem. That is, we examine both the tranditional statistical method which does not take into account spatial correlation and the spatial statistical method which takes into account spatial correlation by applying them to a set of non-stationary spatial data. We compare the predictive powers of these methods. More precisely, we choose Universal Kriging(UK) and Median-Polish Kriging(MPK) as spatial statistical methods, and locally weighted regression or LOESS as a traditional method. As the major criterion for comparison, we use the so-called PRESS statistic, and also draw the prediction surface plot and the prediction standard error surface plot as minor criteria. A real numerical example of non-stantionary spatial data is analyzed for the comparison among the above three methods

Influence Functions in Semivariogram Estimation : A Comparative Study

Spatial data is analyzed in three stages of 1) estimating the variograms, 2) fitting a model for the estimated variograms and 3) predicting the value at unknown location based on the information at known locations (kriging). Recently, it has become a subject of interest to detect influential observations in these stages. Choi and Tanaka(1999) have derived influence functions in the above three stages and have proposed sensitivity analysis procedure. So far influence functions have only been derived for variograms by Gunst and Hartfield(1996). The present article makes a comparison of the performances between those influence functions for variograms derived by Choi and Tanaka(1999) and by Gunst and Hartfield(1996). A real numerical example is given to discuss the validity or usefulness of those influence functions