Live demonstration setup. (Left) The setup consists of a DAVIS346B event camera connected to a standard consumer laptop and undergoes some motion. (Right) The motion estimates are plotted in red and, for rotation-like...
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ISBN:
(纸本)9781665448994
Live demonstration setup. (Left) The setup consists of a DAVIS346B event camera connected to a standard consumer laptop and undergoes some motion. (Right) The motion estimates are plotted in red and, for rotation-like motions, the angular velocities provided by the camera IMU are also plotted in blue. This plot exemplifies an event camera undergoing large rotational motions (up to ~ 1000 deg/s) around the (a) x-axis, (b) y-axis and (c) z-axis. Overall, the incremental motion estimation method follows the IMU measurements. Optionally, the resultant global optical flow can also be shown, as well as the corresponding generated events by accumulating them onto the image plane (bottom left corner).
We have been researching three dimensional (3D) ground-truth systems for performance evaluation of vision and perception systems in the fields of smart manufacturing and robot safety. In this paper we first present an...
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ISBN:
(纸本)9780769549903
We have been researching three dimensional (3D) ground-truth systems for performance evaluation of vision and perception systems in the fields of smart manufacturing and robot safety. In this paper we first present an overview of different systems that have been used to provide ground-truth (GT) measurements and then we discuss the advantages of physically-sensed ground-truth systems for our applications. Then we discuss in detail the three ground- truth systems that we have used in our experiments: ultra wide-band, indoor GPS, and a camera-based motion capture system. Finally, we discuss three different perception-evaluation experiments where we have used these GT systems
We propose SCVRL, a novel contrastive-based framework for self-supervised learning for videos. Differently from previous contrast learning based methods that mostly focus on learning visual semantics (e.g., CVRL), SCV...
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ISBN:
(数字)9781665487399
ISBN:
(纸本)9781665487399
We propose SCVRL, a novel contrastive-based framework for self-supervised learning for videos. Differently from previous contrast learning based methods that mostly focus on learning visual semantics (e.g., CVRL), SCVRL is capable of learning both semantic and motion patterns. For that, we reformulate the popular shuffling pretext task within a modern contrastive learning paradigm. We show that our transformer-based network has a natural capacity to learn motion in self-supervised settings and achieves strong performance, outperforming CVRL on four benchmarks.
We propose a simple yet effective proposal-free architecture for lidar panoptic segmentation. We jointly optimize both semantic segmentation and class-agnostic instance classification in a single network using a pilla...
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ISBN:
(数字)9781665487399
ISBN:
(纸本)9781665487399
We propose a simple yet effective proposal-free architecture for lidar panoptic segmentation. We jointly optimize both semantic segmentation and class-agnostic instance classification in a single network using a pilla-rbased bird's-eye view representation. The instance classification head learns pairwise affinity between pillars to determine whether the pillars belong to the same instance or not. We further propose a local clustering algorithm to propagate instance ids by merging semantic segmentation and affinity predictions. Our experiments on nuScenes dataset show that our approach outperforms previous proposal-free methods and is comparable to proposal-based methods which requires extra annotation from object detection.
We study event-based sensors in the context of spacecraft guidance and control during a descent on Moon-like terrains. For this purpose, we develop a simulator reproducing the event-based camera outputs when exposed t...
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ISBN:
(纸本)9781665448994
We study event-based sensors in the context of spacecraft guidance and control during a descent on Moon-like terrains. For this purpose, we develop a simulator reproducing the event-based camera outputs when exposed to synthetic images of a space environment. We find that it is possible to reconstruct, in this context, the divergence of optical flow vectors (and therefore the time to contact) and use it in a simple control feedback scheme during simulated descents. The results obtained are very encouraging, albeit insufficient to meet the stringent safety constraints and modelling accuracy imposed upon space missions. We thus conclude by discussing future work aimed at addressing these limitations.
Line art plays a fundamental role in illustration and design, and allows for iteratively polishing designs. However, as they lack color, they can have issues in conveying final designs. In this work, we propose an int...
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ISBN:
(纸本)9781665448994
Line art plays a fundamental role in illustration and design, and allows for iteratively polishing designs. However, as they lack color, they can have issues in conveying final designs. In this work, we propose an interactive colorization approach based on a conditional generative adversarial network that takes both the line art and color hints as inputs to produce a high-quality colorized image. Our approach is based on a U-net architecture with a multi-discriminator framework. We propose a Concatenation and Spatial Attention module that is able to generate more consistent and higher quality of line art colorization from user given hints. We evaluate on a large-scale illustration dataset and comparison with existing approaches corroborate the effectiveness of our approach.
Automatic video production of sports aims at producing an aesthetic broadcast of sporting events. We present a new video system able to automatically produce a smooth and pleasant broadcast of Basketball games using a...
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ISBN:
(数字)9781728193601
ISBN:
(纸本)9781728193601
Automatic video production of sports aims at producing an aesthetic broadcast of sporting events. We present a new video system able to automatically produce a smooth and pleasant broadcast of Basketball games using a single fixed 4K camera. The system automatically detects and localizes players, ball and referees, to recognize main action coordinates and game states yielding to a professional cameraman-like production of the basketball event. We also release a fully annotated dataset consisting of single 4K camera and twelve-camera videos of basketball games.
Nowadays, there are outstanding strides towards a future with autonomous vehicles on our roads. While the perception of autonomous vehicles performs well under closed-set conditions, they still struggle to handle the ...
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ISBN:
(纸本)9781665487399
Nowadays, there are outstanding strides towards a future with autonomous vehicles on our roads. While the perception of autonomous vehicles performs well under closed-set conditions, they still struggle to handle the unexpected. This survey provides an extensive overview of anomaly detection techniques based on camera, lidar, radar, multimodal and abstract object level data. We provide a systematization including detection approach, corner case level, ability for an online application, and further attributes. We outline the state-of-the-art and point out current research gaps.
In this paper we analyze the classification performance of neural network structures without parametric inference. Making use of neural architecture search, we empirically demonstrate that it is possible to find rando...
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ISBN:
(纸本)9781665448994
In this paper we analyze the classification performance of neural network structures without parametric inference. Making use of neural architecture search, we empirically demonstrate that it is possible to find random weight architectures, a deep prior, that enables a linear classification to perform on par with fully trained deep counterparts. Through ablation experiments, we exclude the possibility of winning a weight initialization lottery and confirm that suitable deep priors do not require additional inference. In an extension to continual learning, we investigate the possibility of catastrophic interference free incremental learning. Under the assumption of classes originating from the same data distribution, a deep prior found on only a subset of classes is shown to allow discrimination of further classes through training of a simple linear classifier.
Previous research on localizing a target region in an image referred to by a natural language expression has occurred within an object-centric paradigm. However, in practice, there may not be any easily named or ident...
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ISBN:
(纸本)9781728193601
Previous research on localizing a target region in an image referred to by a natural language expression has occurred within an object-centric paradigm. However, in practice, there may not be any easily named or identifiable objects near a target location. Instead, references may need to rely on basic visual attributes, such as color or geometric clues. An expression like "a red something beside a blue vertical line" could still pinpoint a target location. As such, we begin to explore the open challenge of computational object-agnostic reference by constructing a novel dataset and by devising a new set of algorithms that can identify a target region in an image when given a referring expression containing only basic conceptual features.
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