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Virtual worlds as proxy for multi-object tracking analysis

Virtual worlds as proxy for multi-object tracking analysis

引用

2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Gaidon, Adrien Wang, Qiao Cabon, Yohann Vig, Eleonora Computer Vision Group Xerox Research Center Europe France School of Electrical Computer and Energy Engineering School of Arts Media and Engineering Arizona State University United States German Aerospace Center Germany

ISBN: (纸本)9781467388511

Modern computer vision algorithms typically require expensive data acquisition and accurate manual labeling. In this work, we instead leverage the recent progress in computer graphics to generate fully labeled, dynamic, and photo-realistic proxy virtual worlds. We propose an efficient real-to-virtual world cloning method, and validate our approach by building and publicly releasing a new video dataset, called "Virtual KITTI"1, automatically labeled with accurate ground truth for object detection, tracking, scene and instance segmentation, depth, and optical flow. We provide quantitative experimental evidence suggesting that (i) modern deep learning algorithms pre-trained on real data behave similarly in real and virtual worlds, and (ii) pre-training on virtual data improves performance. As the gap between real and virtual worlds is small, virtual worlds enable measuring the impact of various weather and imaging conditions on recognition performance, all other things being equal. We show these factors may affect drastically otherwise high-performing deep models for tracking.

关键词： computer vision

来源：评论

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InterActive: Inter-layer activeness propagation

InterActive: Inter-layer activeness propagation

引用

2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Xie, Lingxi Zheng, Liang Wang, Jingdong Yuille, Alan Tian, Qi Department of Statistics University of California Los Angeles Los AngelesCA United States Department of Computer Science University of Texas at San Antonio San AntonioTX United States Microsoft Research Beijing China Department of Cognitive Science and Computer Science Johns Hopkins University BaltimoreMD United States

ISBN: (纸本)9781467388511

An increasing number of computer vision tasks can be tackled with deep features, which are the intermediate outputs of a pre-trained Convolutional Neural Network. Despite the astonishing performance, deep features extracted from low-level neurons are still below satisfaction, arguably because they cannot access the spatial context contained in the higher layers. In this paper, we present InterActive, a novel algorithm which computes the activeness of neurons and network connections. Activeness is propagated through a neural network in a top-down manner, carrying highlevel context and improving the descriptive power of lowlevel and mid-level neurons. Visualization indicates that neuron activeness can be interpreted as spatial-weighted neuron responses. We achieve state-of-the-art classification performance on a wide range of image datasets.

关键词： Neural networks

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Convolutional two-stream network fusion for video action recognition

Convolutional two-stream network fusion for video action rec...

引用

2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Feichtenhofer, Christoph Pinz, Axel Zisserman, Andrew Graz University of Technology Austria University of Oxford United Kingdom

ISBN: (纸本)9781467388511

Recent applications of Convolutional Neural Networks (ConvNets) for human action recognition in videos have proposed different solutions for incorporating the appearance and motion information. We study a number of ways of fusing ConvNet towers both spatially and temporally in order to best take advantage of this spatio-temporal information. We make the following findings: (i) that rather than fusing at the softmax layer, a spatial and temporal network can be fused at a convolution layer without loss of performance, but with a substantial saving in parameters; (ii) that it is better to fuse such networks spatially at the last convolutional layer than earlier, and that additionally fusing at the class prediction layer can boost accuracy; finally (iii) that pooling of abstract convolutional features over spatiotemporal neighbourhoods further boosts performance. Based on these studies we propose a new ConvNet architecture for spatiotemporal fusion of video snippets, and evaluate its performance on standard benchmarks where this architecture achieves state-of-the-art results.

关键词： Network architecture

来源：评论

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Efficient deep learning for stereo matching

Efficient deep learning for stereo matching

引用

2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Luo, Wenjie Schwing, Alexander G. Urtasun, Raquel Department of Computer Science University of Toronto Canada

ISBN: (纸本)9781467388511

In the past year, convolutional neural networks have been shown to perform extremely well for stereo estimation. However, current architectures rely on siamese networks which exploit concatenation followed by further processing layers, requiring a minute of GPU computation per image pair. In contrast, in this paper we propose a matching network which is able to produce very accurate results in less than a second of GPU computation. Towards this goal, we exploit a product layer which simply computes the inner product between the two representations of a siamese architecture. We train our network by treating the problem as multi-class classification, where the classes are all possible disparities. This allows us to get calibrated scores, which result in much better matching performance when compared to existing approaches.

关键词： Stereo image processing

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A weighted variational model for simultaneous reflectance and illumination estimation

A weighted variational model for simultaneous reflectance an...

引用

2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Fu, Xueyang Zeng, Delu Huang, Yue Zhang, Xiao-Ping Ding, Xinghao Fujian Key Laboratory of Sensing and Computing for Smart City Xiamen University China School of Information Science and Engineering Xiamen University China Department of Electrical and Computer Engineering Ryerson University Canada

ISBN: (纸本)9781467388511

We propose a weighted variational model to estimate both the reflectance and the illumination from an observed image. We show that, though it is widely adopted for ease of modeling, the log-transformed image for this task is not ideal. Based on the previous investigation of the logarithmic transformation, a new weighted variational model is proposed for better prior representation, which is imposed in the regularization terms. Different from conventional variational models, the proposed model can preserve the estimated reflectance with more details. Moreover, the proposed model can suppress noise to some extent. An alternating minimization scheme is adopted to solve the proposed model. Experimental results demonstrate the effectiveness of the proposed model with its algorithm. Compared with other variational methods, the proposed method yields comparable or better results on both subjective and objective assessments.

关键词： pattern recognition

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When VLAD met Hilbert

When VLAD met Hilbert

引用

2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Harandi, Mehrtash Salzmann, Mathieu Porikli, Fatih NICTA Australian National University Canberra Australia Switzerland

ISBN: (纸本)9781467388511

In many challenging visual recognition tasks where training data is limited, Vectors of Locally Aggregated Descriptors (VLAD) have emerged as powerful image/video representations that compete with or outperform state-ofthe-art approaches. In this paper, we address two fundamental limitations of VLAD: its requirement for the local descriptors to have vector form and its restriction to linear classifiers due to its high-dimensionality. To this end, we introduce a kernelized version of VLAD. This not only lets us inherently exploit more sophisticated classification schemes, but also enables us to efficiently aggregate nonvector descriptors (e.g., manifold-valued data) in the VLAD framework. Furthermore, we propose an approximate formulation that allows us to accelerate the coding process while still benefiting from the properties of kernel VLAD. Our experiments demonstrate the effectiveness of our approach at handling manifold-valued data, such as covariance descriptors, on several classification tasks. Our results also evidence the benefits of our nonlinear VLAD descriptors against the linear ones in Euclidean space using several standard benchmark datasets.

关键词： computer vision

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Consistency of silhouettes and their duals

Consistency of silhouettes and their duals

引用

2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Trager, Matthew Hebert, Martial Ponce, Jean Inria France Carnegie Mellon University United States Ecole Normale Superieure PSL Research University France

ISBN: (纸本)9781467388511

Silhouettes provide rich information on three-dimensional shape, since the intersection of the associated visual cones generates the "visual hull", which encloses and approximates the original shape. However, not all silhouettes can actually be projections of the same object in space: this simple observation has implications in object recognition and multi-view segmentation, and has been (often implicitly) used as a basis for camera calibration. In this paper, we investigate the conditions for multiple silhouettes, or more generally arbitrary closed image sets, to be geometrically "consistent". We present this notion as a natural generalization of traditional multi-view geometry, which deals with consistency for points. After discussing some general results, we present a "dual" formulation for consistency, that gives conditions for a family of planar sets to be sections of the same object. Finally, we introduce a more general notion of silhouette "compatibility" under partial knowledge of the camera projections, and point out some possible directions for future research.

关键词： computer vision

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Slow and steady feature analysis: Higher order temporal coherence in video

Slow and steady feature analysis: Higher order temporal cohe...

引用

2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Jayaraman, Dinesh Grauman, Kristen UT Austin United States

ISBN: (纸本)9781467388511

How can unlabeled video augment visual learning? Existing methods perform "slow" feature analysis, encouraging the representations of temporally close frames to exhibit only small differences. While this standard approach captures the fact that high-level visual signals change slowly over time, it fails to capture how the visual content changes. We propose to generalize slow feature analysis to "steady" feature analysis. The key idea is to impose a prior that higher order derivatives in the learned feature space must be small. To this end, we train a convolutional neural network with a regularizer on tuples of sequential frames from unlabeled video. It encourages feature changes over time to be smooth, i.e., similar to the most recent changes. Using five diverse datasets, including unlabeled YouTube and KITTI videos, we demonstrate our method's impact on object, scene, and action recognition tasks. We further show that our features learned from unlabeled video can even surpass a standard heavily supervised pretraining approach.

关键词： computer vision

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Scene recognition with CNNs: Objects, scales and dataset bias

Scene recognition with CNNs: Objects, scales and dataset bia...

引用

2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Herranz, Luis Jiang, Shuqiang Li, Xiangyang Institute of Computer Technology CAS Beijing100190 China

ISBN: (纸本)9781467388511

Since scenes are composed in part of objects, accurate recognition of scenes requires knowledge about both scenes and objects. In this paper we address two related problems: 1) scale induced dataset bias in multi-scale convolutional neural network (CNN) architectures, and 2) how to combine effectively scene-centric and object-centric knowledge (i.e. Places and ImageNet) in CNNs. An earlier attempt, Hybrid-CNN[23], showed that incorporating ImageNet did not help much. Here we propose an alternative method taking the scale into account, resulting in significant recognition gains. By analyzing the response of ImageNet-CNNs and Places-CNNs at different scales we find that both operate in different scale ranges, so using the same network for all the scales induces dataset bias resulting in limited performance. Thus, adapting the feature extractor to each particular scale (i.e. scale-specific CNNs) is crucial to improve recognition, since the objects in the scenes have their specific range of scales. Experimental results show that the recognition accuracy highly depends on the scale, and that simple yet carefully chosen multi-scale combinations of ImageNet-CNNs and Places-CNNs, can push the stateof-the-art recognition accuracy in SUN397 up to 66.26% (and even 70.17% with deeper architectures, comparable to human performance).

关键词： computer vision

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Real-time action recognition with enhanced motion vector CNNs

Real-time action recognition with enhanced motion vector CNN...

引用

2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Zhang, Bowen Wang, Limin Wang, Zhe Qiao, Yu Wang, Hanli Shenzhen Key Lab of Comp. Vis. and Pat. Rec. Shenzhen Institutes of Advanced Technology CAS China Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai China Computer Vision Lab ETH Zurich Switzerland

ISBN: (纸本)9781467388511

The deep two-stream architecture [23] exhibited excellent performance on video based action recognition. The most computationally expensive step in this approach comes from the calculation of optical flow which prevents it to be real-time. This paper accelerates this architecture by replacing optical flow with motion vector which can be obtained directly from compressed videos without extra calculation. However, motion vector lacks fine structures, and contains noisy and inaccurate motion patterns, leading to the evident degradation of recognition performance. Our key insight for relieving this problem is that optical flow and motion vector are inherent correlated. Transferring the knowledge learned with optical flow CNN to motion vector CNN can significantly boost the performance of the latter. Specifically, we introduce three strategies for this, initialization transfer, supervision transfer and their combination. Experimental results show that our method achieves comparable recognition performance to the state-of-the-art, while our method can process 390.7 frames per second, which is 27 times faster than the original two-stream method.

关键词： pattern recognition

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