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...

M.P. Muresan, S. Nedevschi Proceedings of 2019 15th IEEE International Conference on Intelligent Computer Communication and Processing (ICCP), Cluj-Napoca, Romania, September 5-7, 2019, pp. 11-18. The unknown correspondences of measurements and targets, referred to as data association, is one of the main challenges of multi-target tracking. Each new measurement received could be the continuation of some previously detected target, the first detection of a new target or a false alarm. Tracking 3D cuboids, is particularly difficult due to the high amount of data, which can include erroneous or noisy information coming from sensors, that can lead to false measurements, detections...

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...

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....

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....