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Field robotics 2025年 4卷 99-137页

作者： Ayoub Raji Danilo Caporale Francesco Gatti Andrea Giove Micaela Verucchi Davide Malatesta Nicola Musiu Alessandro Toschi Silviu Roberto Popitanu Fabio Bagni Massimiliano Bosi Alexander Liniger Marko Bertogna Daniele Morra Francesco Amerotti Luca Bartoli Federico Martello Riccardo Porta University of Modena and Reggio Emilia Italy University of Parma Italy Technology Innovation Institute-Autonomous Robotics Research Center UAE HIPERT srl University of Pisa Italy HIPERT srl Computer Vision Lab ETH Zurich Switzerland

The Indy Autonomous Challenge (IAC) brought together for the first time in history nine autonomous racing teams competing at an unprecedented speed and in a head-to-head scenario, using independently developed software on open-wheel race cars. This paper presents the complete software architecture used by the team TII EuroRacing (TII-ER), covering all the modules needed to avoid static obstacles, perform active overtakes, and reach speeds above 75m/s (270km/h). In addition to the most common modules related to perception, planning, and control, we discuss the approaches used for vehicle dynamics modeling, simulation, telemetry, and safety. Overall results and the performance of each module are described, as well as the lessons learned during the first two events of the competition on oval tracks, where the team placed second and third, respectively.

关键词： Automobiles Cameras Laser radar Autonomous vehicles Location awareness Safety Planning Vehicle dynamics Testing Sports Motion planning Environmental monitoring

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LiCAR: pseudo-RGB LiDAR image for CAR segmentation

arXiv

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arXiv 2025年

作者： de Loyola Páez-Ubieta, Ignacio Velasco-Sánchez, Edison P. Puente, Santiago T. AUtomatics RObotics and Artificial Vision Lab IUII: University Institute for Computer Research University of Alicante Crta. San Vicente s/n San Vicente del Raspeig AlicanteE-03690 Spain

With the advancement of computing resources, an increasing number of Neural Networks (NNs) are appearing for image detection and segmentation appear. However, these methods usually accept as input a RGB 2D image. On the other side, Light Detection And Ranging (LiDAR) sensors with many layers provide images that are similar to those obtained from a traditional low resolution RGB camera. Following this principle, a new dataset for segmenting cars in pseudo-RGB images has been generated. This dataset combines the information given by the LiDAR sensor into a Spherical Range Image (SRI), concretely the reflectivity, near infrared and signal intensity 2D images. These images are then fed into instance segmentation NNs. These NNs segment the cars that appear in these images, having as result a Bounding Box (BB) and mask precision of 88% and 81.5% respectively with You Only Look Once (YOLO)-v8 large. By using this segmentation NN, some trackers have been applied so as to follow each car segmented instance along a video feed, having great performance in real world experiments. © 2025, CC BY.

关键词： Image segmentation

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OBJECT DETECTION USING HIERARCHICAL GRAPH-BASED SEGMENTATION

OBJECT DETECTION USING HIERARCHICAL GRAPH-BASED SEGMENTATION

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IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Kim, Jungho Choi, Byeongho Kweon, In-So Multimedia IP Research Center KETI Robotics and Computer Vision Lab. KAIST

ISBN: (纸本)9781479903566

Object detection in real images or videos is challenging because the shapes and sizes of objects vary significantly according to their poses, camera viewing direction, and partial occlusion. Previous detection methods employ sliding-window-based schemes that scan windows across an image, requiring many differently shaped windows to capture shape and size variation. In order to solve this problem, we propose an object detection method using hierarchical graph-based segmentation: color-consistent parts are obtained by part-level segmentation and category-consistent regions are found using object-level segmentation. Thus we can avoid scanning a lot of windows across whole images by using part-level segmentation and robustly detect the objects of various shapes and sizes by using object-level segmentation. In addition, we evaluate detection performance using various classifiers with our detection approach.

关键词： Object detection Object classification Graph-based segmentation

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Analysis and query of person-vehicle interactions in homography domain

Analysis and query of person-vehicle interactions in homogra...

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4th ACM International Workshop on Video Surveillance and Sensor Networks, VSSN'06, co-located with the 2006 ACM International Multimedia Conference

作者： Park, Sangho Trivedi, Mohan M. Computer Vision and Robotics Research Lab. University of California San Diego CA United States

ISBN: (纸本)1595934960

This paper presents an efficient and robust paradigm for analysis and query of moving-object interactions in planar homography *** and vehicle activities/interactions are analyzed for situational awareness by using a multi-perspective *** homography constraints are exploited to extract view-invariant object features including footage area and velocity of objects on the ground plane. Spatio-temporal relationships between person-and vehicle-tracks are represented by a semantic event grammar. Semantic-level information of the situation is achieved with the anticipation of possible directions of near-future tracks using piecewise velocity history. An efficient query paradigm is proposed by histogram-based approximation of probability density functions of objects and by quad-tree indexing. Experimental data show promising *** framework can be applied to applications for enhanced situational awareness such as disaster prevention,human interactions in structured environments,and crowd movement analysis in wide-view areas. Copyright 2006 ACM.

关键词： Data reduction

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Attention estimation by simultaneous observation of viewer and view

Attention estimation by simultaneous observation of viewer a...

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2010 IEEE computer Society Conference on computer vision and Pattern Recognition - Workshops, CVPRW 2010

作者： Doshi, Anup Trivedi, Mohan M. Computer Vision and Robotics Research Lab University of California San Diego CA 92093-0434 United States

ISBN: (纸本)9781424470297

We introduce a new approach to analyzing the attentive state of a human subject, given cameras focused on the subject and their environment. In particular, the task of analyzing the focus of attention of a human driver is of primary concern. Up to 80% of automobile crashes are related to driver inattention;thus it is important for an Intelligent Driver Assistance System (IDAS) to be aware of the driver state. We present a new Bayesian paradigm for estimating human attention specifically addressing the problems arising in dynamic situations. The model incorporates vision-based gaze estimation, "top-down"-and "bottomup"-based visual saliency maps, and cognitive considerations such as inhibition of return and center bias that affect the relationship between gaze and attention. Results demonstrate the validity on real driving data, showing quantitative improvements over systems using only gaze or only saliency, and elucidate the value of such a model for any human-machine interface. © 2010 IEEE.

关键词： Accidents

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Radiometric calibration by transform invariant low-rank structure

Radiometric calibration by transform invariant low-rank stru...

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作者： Lee, Joon-Young Shi, Boxin Matsushita, Yasuyuki Kweon, In-So Ikeuchi, Katsushi Robotics and Computer Vision Lab KAIST Korea Republic of University of Tokyo Japan Microsoft Research Asia China

ISBN: (纸本)9781457703942

We present a robust radiometric calibration method that capitalizes on the transform invariant low-rank structure of sensor irradiances recorded from a static scene with different exposure times. We formulate the radiometric calibration problem as a rank minimization problem. Unlike previous approaches, our method naturally avoids over-fitting problem;therefore, it is robust against biased distribution of the input data, which is common in practice. When the exposure times are completely unknown, the proposed method can robustly estimate the response function up to an exponential ambiguity. The method is evaluated using both simulation and real-world datasets and shows a superior performance than previous approaches. © 2011 IEEE.

关键词： Radiometry

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A two-stage multi-view analysis framework for human activity and interactions

A two-stage multi-view analysis framework for human activity...

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2007 IEEE Workshop on Motion and Video Computing, WMVC 2007

作者： Park, Sangho Trivedi, Mohan M. Computer Vision and Robotics Research Lab. University of California San Diego San Diego CA 92037

ISBN: (纸本)0769527930

This paper presents a new framework for a multi-stage multi-view approach for human interactions and activity analysis. The analysis is performed in a distributed vision system that synergistically integrate track- and body-level representations across multiple cameras. Our system aims at versatile and easily-deployable system that does not require careful camera calibration. Main contributions of the paper are: (1) context-dependent camera handover for occlusion handling, (2) switching the multi-stage analysis between track- and body-level representations, and (3) a hypothesis-verification paradigm for top-down feedback exploiting spatio-temporal constraints inherent in human interaction. Experimental evaluation shows the efficacy of the proposed system for analyzing multi-person interactions. Current implementation uses two views, but extension to more views is straightforward. © 2007 IEEE.

关键词： Human computer interaction

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2013 IEEE Conference on computer vision and Pattern Recognition Workshops, CVPRW 2013

作者： Ohn-Bar, Eshed Trivedi, Mohan M. Computer Vision and Robotics Research Lab University of California San Diego CA 92093-0434 United States

ISBN: (纸本)9780769549903

We propose a set of features derived from skeleton tracking of the human body and depth maps for the purpose of action recognition. The descriptors proposed are easy to implement, produce relatively small-sized feature sets, and the multi-class classification scheme is fast and suitable for real-time applications. We intuitively characterize actions using pairwise affinities between view-invariant joint angles features over the performance of an action. Additionally, a new descriptor for spatio-temporal feature extraction from color and depth images is introduced. This descriptor involves an application of a modified histogram of oriented gradients (HOG) algorithm. The application produces a feature set at every frame, and these features are collected into a 2D array which then the same algorithm is applied to again (the approach is termed HOG2). Both feature sets are evaluated in a bag-of-words scheme using a linear SVM, showing state-of-the-art results on public datasets from different domains of human-computer interaction. © 2013 IEEE.

关键词： Human computer interaction

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Homography-based analysis of people and vehicle activities in crowded scenes

Homography-based analysis of people and vehicle activities i...

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7th IEEE Workshop on Applications of computer vision, WACV 2007

作者： Park, Sangho Trivedi, Mohan M. Computer Vision and Robotics Research Lab. University of California San Diego San Diego CA 92037

ISBN: (纸本)0769527949

This paper presents an new framework for homographybased analysis of pedestrian-vehicle activity in crowded scenes. Planar homography constraint is exploited to extract view-invariant object features including footage area and velocity of objects on the ground plane. Spatiotemporal relationships between people- and vehicle- tracks are represented by a semantic event. Context awareness of the situation is achieved by the estimated density distribution of objects and the anticipation of possible directions of near-future tracks using piecewise velocity history. Singleview and multi-view based homography mapping options are compared. Our framework can be used to enhance situational awareness for disaster prevention, human interactions in structured environments, and crowd movement analysis at wide regions. © 2007 IEEE.

关键词： Feature extraction

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Stereo matching with color and monochrome cameras in low-light conditions

Stereo matching with color and monochrome cameras in low-lig...

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2016 IEEE Conference on computer vision and Pattern Recognition, CVPR 2016

作者： Jeon, Hae-Gon Lee, Joon-Young Im, Sunghoon Ha, Hyowon Kweon, In So Robotics and Computer Vision Lab. KAIST Korea Republic of Adobe Research United States

ISBN: (纸本)9781467388511

Consumer devices with stereo cameras have become popular because of their low-cost depth sensing capability. However, those systems usually suffer from low imaging quality and inaccurate depth acquisition under low-light conditions. To address the problem, we present a new stereo matching method with a color and monochrome camera pair. We focus on the fundamental trade-off that monochrome cameras have much better light-efficiency than color-filtered cameras. Our key ideas involve compensating for the radiometric difference between two crossspectral images and taking full advantage of complementary data. Consequently, our method produces both an accurate depth map and high-quality images, which are applicable for various depth-aware image processing. Our method is evaluated using various datasets and the performance of our depth estimation consistently outperforms state-of-the-art methods.

关键词： Stereo image processing

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