|
|
David Geronimo, Angel Sappa, Daniel Ponsa, & Antonio Lopez. (2010). 2D-3D based on-board pedestrian detection system. CVIU - Computer Vision and Image Understanding, 114(5), 583–595.
Abstract: During the next decade, on-board pedestrian detection systems will play a key role in the challenge of increasing traffic safety. The main target of these systems, to detect pedestrians in urban scenarios, implies overcoming difficulties like processing outdoor scenes from a mobile platform and searching for aspect-changing objects in cluttered environments. This makes such systems combine techniques in the state-of-the-art Computer Vision. In this paper we present a three module system based on both 2D and 3D cues. The first module uses 3D information to estimate the road plane parameters and thus select a coherent set of regions of interest (ROIs) to be further analyzed. The second module uses Real AdaBoost and a combined set of Haar wavelets and edge orientation histograms to classify the incoming ROIs as pedestrian or non-pedestrian. The final module loops again with the 3D cue in order to verify the classified ROIs and with the 2D in order to refine the final results. According to the results, the integration of the proposed techniques gives rise to a promising system.
Keywords: Pedestrian detection; Advanced Driver Assistance Systems; Horizon line; Haar wavelets; Edge orientation histograms
|
|
|
|
Fernando Barrera, Felipe Lumbreras, & Angel Sappa. (2012). Multimodal Stereo Vision System: 3D Data Extraction and Algorithm Evaluation. J-STSP - IEEE Journal of Selected Topics in Signal Processing, 6(5), 437–446.
Abstract: This paper proposes an imaging system for computing sparse depth maps from multispectral images. A special stereo head consisting of an infrared and a color camera defines the proposed multimodal acquisition system. The cameras are rigidly attached so that their image planes are parallel. Details about the calibration and image rectification procedure are provided. Sparse disparity maps are obtained by the combined use of mutual information enriched with gradient information. The proposed approach is evaluated using a Receiver Operating Characteristics curve. Furthermore, a multispectral dataset, color and infrared images, together with their corresponding ground truth disparity maps, is generated and used as a test bed. Experimental results in real outdoor scenarios are provided showing its viability and that the proposed approach is not restricted to a specific domain.
|
|
|
|
Jiaolong Xu, David Vazquez, Antonio Lopez, Javier Marin, & Daniel Ponsa. (2014). Learning a Part-based Pedestrian Detector in Virtual World. TITS - IEEE Transactions on Intelligent Transportation Systems, 15(5), 2121–2131.
Abstract: Detecting pedestrians with on-board vision systems is of paramount interest for assisting drivers to prevent vehicle-to-pedestrian accidents. The core of a pedestrian detector is its classification module, which aims at deciding if a given image window contains a pedestrian. Given the difficulty of this task, many classifiers have been proposed during the last fifteen years. Among them, the so-called (deformable) part-based classifiers including multi-view modeling are usually top ranked in accuracy. Training such classifiers is not trivial since a proper aspect clustering and spatial part alignment of the pedestrian training samples are crucial for obtaining an accurate classifier. In this paper, first we perform automatic aspect clustering and part alignment by using virtual-world pedestrians, i.e., human annotations are not required. Second, we use a mixture-of-parts approach that allows part sharing among different aspects. Third, these proposals are integrated in a learning framework which also allows to incorporate real-world training data to perform domain adaptation between virtual- and real-world cameras. Overall, the obtained results on four popular on-board datasets show that our proposal clearly outperforms the state-of-the-art deformable part-based detector known as latent SVM.
Keywords: Domain Adaptation; Pedestrian Detection; Virtual Worlds
|
|
|
|
Aura Hernandez-Sabate, Jose Elias Yauri, Pau Folch, Miquel Angel Piera, & Debora Gil. (2022). Recognition of the Mental Workloads of Pilots in the Cockpit Using EEG Signals. APPLSCI - Applied Sciences, 12(5), 2298.
Abstract: The commercial flightdeck is a naturally multi-tasking work environment, one in which interruptions are frequent come in various forms, contributing in many cases to aviation incident reports. Automatic characterization of pilots’ workloads is essential to preventing these kind of incidents. In addition, minimizing the physiological sensor network as much as possible remains both a challenge and a requirement. Electroencephalogram (EEG) signals have shown high correlations with specific cognitive and mental states, such as workload. However, there is not enough evidence in the literature to validate how well models generalize in cases of new subjects performing tasks with workloads similar to the ones included during the model’s training. In this paper, we propose a convolutional neural network to classify EEG features across different mental workloads in a continuous performance task test that partly measures working memory and working memory capacity. Our model is valid at the general population level and it is able to transfer task learning to pilot mental workload recognition in a simulated operational environment.
Keywords: Cognitive states; Mental workload; EEG analysis; Neural networks; Multimodal data fusion
|
|
|
|
Daniel Ponsa, Robert Benavente, Felipe Lumbreras, Judit Martinez, & Xavier Roca. (2003). Quality control of safety belts by machine vision inspection for real-time production. Optical Engineering (IF: 0.877), 42(4), 1114–1120.
|
|
