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GraphTrack: Fast and globally optimal tracking in videos

GraphTrack: Fast and globally optimal tracking in videos

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作者： Amberg, Brian Vetter, Thomas University of Basel Department of Mathematics and Computer Science Switzerland

ISBN: (纸本)9781457703942

In video post-production it is often necessary to track interest points in the video. This is called off-line tracking, because the complete video is available to the algorithm and can be contrasted with on-line tracking, where an incoming stream is tracked in real time. Off-line tracking should be accurate and-if used interactively-needs to be fast, preferably faster than real-time. We describe a 50 to 100 frames per second off-line tracking algorithm, which globally maximizes the probability of the track given the complete video. The algorithm is more reliable than previous methods because it explains the complete frames, not only the patches of the final track, making as much use of the data as possible. It achieves efficiency by using a greedy search strategy with deferred cost evaluation, focusing the computational effort on the most promising track candidates while finding the globally optimal track. © 2011 ieee.

关键词： computers

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Adaptive polynomial modelling of the reflectance map for shape estimation from stereo and shading

Adaptive polynomial modelling of the reflectance map for sha...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Hougen Ahuja Beckman Institute and Coordinated Sciences Laboratory University of Illinois Urbana IL USA

This paper is concerned with estimation of the reflectance map and its use in surface shape estimation by a method that combines stereo and shading information. There are many advantages to the integrated approach. However, shape from shading algorithms are limited in their applicability by the assumption of idealized reflectance and lighting models involving many preset or hard to estimate parameters. In this paper, it is shown that the difficulties involved in estimation of the reflectance function and light source distribution can be eliminated through direct estimation of the reflectance map using adaptive polynomial models. The reflectance map estimate is used with a surface estimate provided by stereo in an integrated approach to surface shape estimation.< >

关键词： Stereo vision Polynomial approximation

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Generalized Focal Loss V2: Learning Reliable Localization Quality Estimation for Dense Object Detection

Generalized Focal Loss V2: Learning Reliable Localization Qu...

引用

2021 ieee/CVF conference on computer vision and pattern recognition, CVPR 2021

作者： Li, Xiang Wang, Wenhai Hu, Xiaolin Li, Jun Tang, Jinhui Yang, Jian Nanjing University of Science and Technology Momenta Nanjing University Tsinghua University

Localization Quality Estimation (LQE) is crucial and popular in the recent advancement of dense object detectors since it can provide accurate ranking scores that benefit the Non-Maximum Suppression processing and improve detection performance. As a common practice, most existing methods predict LQE scores through vanilla convolutional features shared with object classification or bounding box regression. In this paper, we explore a completely novel and different perspective to perform LQE - based on the learned distributions of the four parameters of the bounding box. The bounding box distributions are inspired and introduced as “General Distribution” in GFLV1, which describes the uncertainty of the predicted bounding boxes well. Such a property makes the distribution statistics of a bounding box highly correlated to its real localization quality. Specifically, a bounding box distribution with a sharp peak usually corresponds to high localization quality, and vice versa. By leveraging the close correlation between distribution statistics and the real localization quality, we develop a considerably lightweight Distribution-Guided Quality Predictor (DGQP) for reliable LQE based on GFLV1, thus producing GFLV2. To our best knowledge, it is the first attempt in object detection to use a highly relevant, statistical representation to facilitate LQE. Extensive experiments demonstrate the effectiveness of our method. Notably, GFLV2 (ResNet-101) achieves 46.2 AP at 14.6 FPS, surpassing the previous state-of-the-art ATSS baseline (43.6 AP at 14.6 FPS) by absolute 2.6 AP on COCO test-dev, without sacrificing the efficiency both in training and inference. © 2021 ieee

关键词： Location awareness Training computer vision Uncertainty Correlation Estimation Object detection

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A Visibility Improvement System for Low vision Drivers by Nonlinear Enhancement of Fused Visible and Infrared Video

A Visibility Improvement System for Low Vision Drivers by No...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Hau Ngo Li Tao Ming Zhang A. Livingston V. Asari Computational Intelligence and Machine Vision Laboratory Department of Electrical and Computer Engineering Old Dominion University Norfolk VA USA

Development of a visibility improvement system for helping drivers with poor vision during night and bad weather conditions is proposed in this paper. Video streams captured by a CCD camera and an infrared camera are registered and fused to obtain image details in extremely low lighting conditions. A novel image enhancement technique employing a nonlinear expansion function is developed for enhancing the fused images. The slope characteristics of the expansion function at an individual pixel location is based on the statistical properties of its neighborhood pixels. The expansion function is also capable of reducing the intensity of the overly bright image regions due to the presence of head lights from vehicles in opposite direction. The enhanced video stream is then subjected to a color restoration process to introduce natural color using the color information gathered from the CCD camera image. The entire image processing and analysis system is being installed in an FPGA environment for performing the processing in real-time. The processed video stream is displayed on a Head-Up Display located directly in front of the driver to assist him/her to drive safely in poor visibility environments. Preliminary results obtained in various experiments conducted with the proposed system are encouraging.

关键词： Machine vision Streaming media Charge-coupled image sensors Color Charge coupled devices Infrared imaging Image enhancement Head Vehicles Image restoration

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DR(eye)VE: A Dataset for Attention-Based Tasks with Applications to Autonomous and Assisted Driving

DR(eye)VE: A Dataset for Attention-Based Tasks with Applicat...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Stefano Alletto Andrea Palazzi Francesco Solera Simone Calderara Rita Cucchiara University of Modena and Reggio Emilia

Autonomous and assisted driving are undoubtedly hot topics in computer vision. However, the driving task is extremely complex and a deep understanding of drivers' behavior is still lacking. Several researchers are now investigating the attention mechanism in order to define computational models for detecting salient and interesting objects in the scene. Nevertheless, most of these models only refer to bottom up visual saliency and are focused on still images. Instead, during the driving experience the temporal nature and peculiarity of the task influence the attention mechanisms, leading to the conclusion that real life driving data is mandatory. In this paper we propose a novel and publicly available dataset acquired during actual driving. Our dataset, composed by more than 500,000 frames, contains drivers' gaze fixations and their temporal integration providing task-specific saliency maps. Geo-referenced locations, driving speed and course complete the set of released data. To the best of our knowledge, this is the first publicly available dataset of this kind and can foster new discussions on better understanding, exploiting and reproducing the driver's attention process in the autonomous and assisted cars of future generations.

关键词： Cameras Visualization Computational modeling Automobiles computer vision Context

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Height recovery from intensity gradient

Height recovery from intensity gradient

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Ruo Zhang Shah Computer Science Department University of Central Florida Orlando FL USA

Unlike existing global shape-from-shading algorithms which involve the brightness constraint in their formulation, we propose a new algorithm which replaces the brightness constraint by an intensity gradient constraint. This is a global approach which obtains the solution by the minimization of an error function over the entire image. Through the linearization of the reflectance map and the discretization of the surface gradient, the intensity gradient can be expressed as a linear function of the surface height. A quadratic error function, which involves the intensity gradient constraint and the traditional smoothness constraint, is minimized efficiently by solving a sparse linear system using the multigrid technique. Neither the information at singular points nor the information at occluding boundaries is needed for the initialization.< >

关键词： Image reconstruction Differential equations Minimization methods Machine vision

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Object tracking via dual linear structured SVM and explicit feature map

Object tracking via dual linear structured SVM and explicit ...

引用

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

作者： Ning, Jifeng Yang, Jimei Jiang, Shaojie Zhang, Lei Yang, Ming-Hsuan College of Information Engineering Northwest A and F University China Adobe Research United States Department of Computing Hong Kong Polytechnic University China Electrical Engineering and Computer Science University of California Merced United States

ISBN: (纸本)9781467388511

Structured support vector machine (SSVM) based methods have demonstrated encouraging performance in recent object tracking benchmarks. However, the complex and expensive optimization limits their deployment in real-world applications. In this paper, we present a simple yet efficient dual linear SSVM (DLSSVM) algorithm to enable fast learning and execution during tracking. By analyzing the dual variables, we propose a primal classifier update formula where the learning step size is computed in closed form. This online learning method significantly improves the robustness of the proposed linear SSVM with lower computational cost. Second, we approximate the intersection kernel for feature representations with an explicit feature map to further improve tracking performance. Finally, we extend the proposed DLSSVM tracker with multi-scale estimation to address the "drift" problem. Experimental results on large benchmark datasets with 50 and 100 video sequences show that the proposed DLSSVM tracking algorithm achieves state-of-the-art performance.

关键词： Support vector machines

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iLab-20M: A large-scale controlled object dataset to investigate deep learning

iLab-20M: A large-scale controlled object dataset to investi...

引用

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

作者： Borji, Ali Izadi, Saeed Itti, Laurent Center for Research in Computer Vision University of Central Florida United States Amirkabir University of Technology Iran University of Southern California United States

ISBN: (纸本)9781467388511

Tolerance to image variations (e.g., translation, scale, pose, illumination, background) is an important desired property of any object recognition system, be it human or machine. Moving towards increasingly bigger datasets has been trending in computer vision especially with the emergence of highly popular deep learning models. While being very useful for learning invariance to object inter- and intra-class shape variability, these large-scale wild datasets are not very useful for learning invariance to other parameters urging researchers to resort to other tricks for training models. In this work, we introduce a large-scale synthetic dataset, which is freely and publicly available, and use it to answer several fundamental questions regarding selectivity and invariance properties of convolutional neural networks. Our dataset contains two parts: a) objects shot on a turntable: 15 categories, 8 rotation angles, 11 cameras on a semi-circular arch, 5 lighting conditions, 3 focus levels, variety of backgrounds (23.4 per instance) generating 1320 images per instance (about 22 million images in total), and b) scenes: in which a robotic arm takes pictures of objects on a 1:160 scale scene. We study: 1) invariance and selectivity of different CNN layers, 2) knowledge transfer from one object category to another, 3) systematic or random sampling of images to build a train set, 4) domain adaptation from synthetic to natural scenes, and 5) order of knowledge delivery to CNNs. We also discuss how our analyses can lead the field to develop more efficient deep learning methods.

关键词： Deep learning

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Sequential Reliable-Inference for Rapid Detection of Human Actions

Sequential Reliable-Inference for Rapid Detection of Human A...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： J.W. Davis Department of Computer Science and Engineering Ohio State Uinversity Columbus OH USA

We present a probabilistic reliable-inference framework to address the issue of rapid-and-reliable detection of human actions. The approach determines the shortest video exposure needed for low-latency recognition by sequentially evaluating a series of posterior class ratios to find the earliest reliable decision point. Results are presented for a set of people walking, running, and standing at different styles and multiple viewpoints, and compared to an alternative ML approach.

关键词： Humans Legged locomotion Robot vision systems Cameras computer science Reliability engineering Surveillance Video recording Mobile robots Robotics and automation

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Robust Three-View Triangulation Done Fast

Robust Three-View Triangulation Done Fast

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Johan Hedborg Andreas Robinson Michael Felsberg Linkoping University

Estimating the position of a 3-dimensional world point given its 2-dimensional projections in a set of images is a key component in numerous computer vision systems. There are several methods dealing with this problem, ranging from sub-optimal, linear least square triangulation in two views, to finding the world point that minimized the L2-reprojection error in three views. This leads to the statistically optimal estimate under the assumption of Gaussian noise. In this paper we present a solution to the optimal triangulation in three views. The standard approach for solving the three-view triangulation problem is to find a closed-form solution. In contrast to this, we propose a new method based on an iterative scheme. The method is rigorously tested on both synthetic and real image data with corresponding ground truth, on a midrange desktop PC and a Raspberry Pi, a low-end mobile platform. We are able to improve the precision achieved by the closed-form solvers and reach a speed-up of two orders of magnitude compared to the current state-of-the-art solver. In numbers, this amounts to around 300K triangulations per second on the PC and 30K triangulations per second on Raspberry Pi.

关键词： Three-dimensional displays Polynomials computer vision Cameras conferences Robustness Noise

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