The UP-Drive project resulted in 42 scientific publications in journals, international conferences and workshops. The blog entries below provide an overview of these publications including links to their open access versions.
J. Škovierová, A. Vobecký, M. Uller, R. Škoviera, V. Hlaváč 4th International Conference on Vehicle Technology and Intelligent Transport Systems (VEHITS 2018) The reported work contributes to the self-driving car efforts, more specifically to scenario understanding from the ego-car point of view. We focus on estimating the intentions of pedestrians near a zebra crossing. First,we predict the future motion of detected pedestrians in a three seconds time horizon. Second, we estimate the intention of each pedestrian to cross the street using a Bayesian network. Results indicate, that the dependence between the error rate of motion prediction and the intention estimation...
Robert Varga Deep learning workshop – 13th IEEE International Conference on Intelligent Computer Communication and Processing (ICCP 2017) Slides (.pdf)
Vlad Miclea Deep learning workshop – 2017 13th IEEE International Conference on Intelligent Computer Communication and Processing (ICCP 2017) Slides (.pdf)
Andra Petrovai Deep learning workshop – 13th IEEE International Conference on Intelligent Computer Communication and Processing (ICCP 2017) Slides (.pdf)
Arthur Costea Deep learning workshop – 13th IEEE International Conference on Intelligent Computer Communication and Processing (ICCP 2017) Slides (.pdf)
J. Moravec, R. Sara Proceedings of the 23rd Computer Vision Winter Workshop, Cesky Krumlov, Czech Republic, pp. 27-35. February 5-7, 2018. We present a novel method for online LiDAR–Camera system calibration tracking and refinement. The method is correspondence-free, formulated as a maximum-likelihood learning task. It is based on a consistency of projected LiDAR point cloud corners and optical image edges. The likelihood function is robustified using a model in which the inlier /outlier label for the image edge pixel is marginalized out. The learning is performed by a stochastic on-line algorithm that includes a delayed learning mechanism improving its stability....
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...
