V. Miclea, S. Nedevschi Proceedings of 2018 IEEE Intelligent Vehicles Symposium (IV), Changshu, 2018, pp. 300-306. We propose a new real-time depth upsampling method based on convolutional neural networks (CNNs) that uses the local context provided by semantic information. Two solutions based on convolutional networks are introduced, modeled according to the level of sparsity given by the depth sensor. While first CNN upsamples data from a partial-dense input, the second one uses dilated convolutions as means to cope with sparse inputs from cost-effective depth sensors. Experiments over data extracted from Kitti dataset highlight the performance of our methods while running...

S.E.C. Goga, S. Nedevschi Proceedings of 2018 14th IEEE International Conference on Intelligent Computer Communication and Processing (ICCP), Cluj-Napoca, Romania, September 7-9, 2018, pp. 301-308. This article presents a new approach for detecting curbs in urban environments. It is based on the fusion between semantic labeled images obtained using a convolutional neural network and a LiDAR point cloud. Semantic information will be used in order to exploit context for the detection of urban curbs. Using only the semantic labels associated to 3D points, we will define a set of 3D ROIs in which curbs are most likely to reside, thus...

F. Vancea, S. Nedevschi Proceedings of 2018 14th IEEE International Conference on Intelligent Computer Communication and Processing (ICCP), Cluj-Napoca, Romania, September 7-9, 2018, pp. 259-264. Vehicle taillight detection is an important topic in the fields of collision avoidance systems and autonomous vehicles. By analyzing the changes in the taillights of vehicles, the intention of the driver can be understood, which can prevent possible accidents. This paper presents a convolutional neural network architecture capable of segmenting taillight pixels by detecting vehicles and uses already computed features to segment taillights. The network is composed of a Faster RCNN that detects vehicles and...

A.D. Costea, A. Petrovai, S. Nedevschi Deep vision workshop; 2018 IEEE Conference on Computer Vision and Pattern Recognition (CVPR 18) A powerful scene understanding can be achieved by combining the tasks of semantic segmentation and instance level recognition. Considering that these tasks are complementary, we propose a multi-objective fusion scheme which leverages the capabilities of each task: pixel level semantic segmentation performs well in background classification and delimiting foreground objects from background, while instance level segmentation excels in recognizing and classifying objects as a whole. We use a fully convolutional residual network together with a feature pyramid network in order...

H. Florea, R. Varga, S. Nedevschi Proceedings of 2018 14th IEEE International Conference on Intelligent Computer Communication and Processing (ICCP), Cluj-Napoca, Romania, September 7-9, 2018, pp. 223-228. This paper discusses the architecture of an environment perception system for autonomous vehicles. The modules of the system are described briefly and we focus on important changes in the architecture that enable: decoupling of data acquisition from data processing; synchronous data processing; parallel computation on GPU and multiple CPU cores; efficient data passing using pointers; adaptive architecture capable of working with different number of sensors. The experimental results compare execution times before and...

F. Oniga, S. Nedevschi Proceedings of 2018 14th IEEE International Conference on Intelligent Computer Communication and Processing (ICCP), Cluj-Napoca, Romania, September 7-9, 2018, pp. 209-214. A low complexity approach for computing the orientation of 3D obstacles, detected from lidar data, is proposed in this paper. The proposed method takes as input obstacles represented as cuboids without orientation (aligned with the reference frame). Each cuboid contains a cluster of obstacle locations (discrete grid cells). First, for each obstacle, the boundaries that are visible for the perception system are selected. A model consisting of two perpendicular lines is fitted to the set...