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....
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....
M.P. Muresan, I. Giosan, S. Nedevschi Sensors 2020, 20, 1110; doi:10.3390/s20041110, pp. 1-33. The stabilization and validation process of the measured position of objects is an important step for high‐level perception functions and for the correct processing of sensory data. The goal of this process is to detect and handle inconsistencies between different sensor measurements, which result from the perception system. The aggregation of the detections from different sensors consists in the combination of the sensorial data in one common reference frame for each identified object, leading to the creation of a super‐sensor. The result of the data aggregation may...
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...
V.C. Miclea, S. Nedevschi IEEE Transactions on Intelligent Transportation Systems (Early Access), pp. 1-11, 2019, DOI: 10.1109/TITS.2019.2913883. In this paper, we propose a novel semantic segmentation-based stereo reconstruction method that can keep up with the accuracy of the state-of-the art approaches while running in real time. The solution follows the classic stereo pipeline, each step in the stereo workflow being enhanced by additional information from semantic segmentation. Therefore, we introduce several improvements to computation, aggregation, and optimization by adapting existing techniques to integrate additional surface information given by each semantic class. For the cost computation and optimization steps, we propose...
