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...
M.P. Muresan, S. Nedevschi, I. Giosan Proceedings of 2017 13th IEEE International Conference on Intelligent Computer Communication and Processing (ICCP), Cluj-Napoca, Romania, September 7-9, 2017, pp. 317-322. The robust detection of obstacles, on a given road path by vehicles equipped with range measurement devices represents a requirement for many research fields including autonomous driving and advanced driving assistance systems. One particular sensor system used for measurement tasks, due to its known accuracy, is the LIDAR (Light Detection and Ranging). The commercial price and computational intensiveness of such systems generally increase with the number of scanning layers. For this reason, in...
S.E.C. Goga, S. Nedevschi Proceedings of 2017 13th IEEE International Conference on Intelligent Computer Communication and Processing (ICCP), Cluj-Napoca, Romania, September 7-9, 2017, pp. 309-315. This paper proposes a novel approach for segmenting and space partitioning data ofsparse 3D LiDAR point clouds for autonomous driving tasks in urban environments. Our main focus is building a compact data representation which provides enough information for an accurate segmentation algorithm. We propose the use of an extension of elevation maps for automotive driving perception tasks which is capable of dealing with both protruding and hanging objects found in urban scenes like bridges, hanging...
B.C.Z. Blaga, S. Nedevschi Proceedings of 2017 13th IEEE International Conference on Intelligent Computer Communication and Processing (ICCP), Cluj-Napoca, Romania, September 7-9, 2017, pp. 295-301. In an autonomous driving system, drift can affect the sensor’s position, introducing errors in the extrinsic calibration. For this reason, we have developed a method which continuously monitors two sensors, camera, and LIDAR with 16 beams, and adjusts the value of their cross-calibration. Our algorithm, starting from correct values of the extrinsic crosscalibration parameters, can detect small sensor drift during vehicle driving, by overlapping the edges from the LIDAR over the edges from the image....
Danila Rukhovich, Daniel Mouritzen, Ralf Kaestner, Martin Rufli, Alexander Velizhev International Conference on Computer Vision (ICCV) 2019 – Workshop on Computer Vision for Road Scene Understanding and Autonomous Driving This paper addresses the problem of scale estimation in monocular SLAM by estimating absolute distances between camera centers of consecutive image frames. These estimates would improve the overall performance of classical(not deep) SLAM systems and allow metric feature locations to be recovered from a single monocular camera. We propose several network architectures that lead to an improvement of scale estimation accuracy over the state of the art. In addition, we exploit...
A. Petrovai, S. Nedevschi Proceeding of 2019 IEEE Intelligent Transportation Systems Conference (ITSC), Auckland, New Zeeland, 26-30 October,2019, pp. 2394-2401. In this paper, we tackle the newly introduced panoptic segmentation task. Panoptic segmentation unifies semantic and instance segmentation and leverages the capabilities of these complementary tasks by providing pixel and instance level classification. Current state-of-the-art approaches employ either separate networks for each task or a single network for both task and post processing heuristics fuse the outputs into the final panoptic segmentation. Instead, our approach solves all three tasks including panoptic segmentation with an end-to-end learnable fully convolutional neural network....
S.E.C. Deac, I. Giosan, S. Nedevschi Proceeding of 2019 IEEE Intelligent Transportation Systems Conference (ITSC), Auckland, New Zeeland, 26-30 October,2019, pp. 3433-3440. In this paper we propose a robust curb detection method which is based on the fusion between semantically labeled camera images and a 3D point cloud coming from LiDAR sensors. The labels from the semantically enhanced cloud are used to reduce the curbs’ searching area. Several spatial cues are next computed on each candidate curb region. Based on these features, a candidate curb region is either rejected or refined for obtaining a precise positioning of the curb points...
A. Petrovai, S. Nedevschi Proceeding of 2019 IEEE Intelligent Vehicles Symposium (IV 2019), Paris, France, 9 – 12 June, 2019, pp. 2575-2581. Environment perception for automated vehicles is achieved by fusing the outputs of different sensors such as cameras, LIDARs and RADARs. Images provide a semantic understanding of the environment at object level using instance segmentation, but also at background level using semantic segmentation. We propose a fully convolutional residual network based on Mask R-CNN to achieve both semantic and instance level recognition. We aim at developing an efficient network that could run in real-time for automated driving applications without...
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...
