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Real-time tracking with multiple cues by set theoretic random search

Real-time tracking with multiple cues by set theoretic rando...

引用

conference on computer vision and pattern recognition (cvpr)

作者： Cheng Chang R. Ansari Microsoft Corporation USA ECE Department University of Illinois Chicago IL USA

Conventional treatment of visual tracking has been to optimize an objective function in a probabilistic framework. In this formulation, efficient algorithms employing simple prior distributions are usually insufficient to handle clutters (e.g., Kalman filter). On the other hand, distributions that are complex enough to incorporate all a priori knowledge can make the problem computationally intractable (e.g., particle filters (PF)). This paper proposes a new formulation of visual tracking where every piece of information, be it from a priori knowledge or observed data, is represented by a set in the solution space and the intersection of these sets, the feasibility set, represents all acceptable solutions. Based on this formulation, we propose an algorithm whose objective is to find a solution in the feasibility set. We show that this set theoretic tracking algorithm performs effective face tracking and is computationally more efficient than standard PF-based tracking.

关键词： Target tracking Particle filters Bayesian methods Stochastic processes computer vision Robustness Shape measurement Particle tracking Optimization methods Distributed computing

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First Steps towards Stereo-based 6DOF SLAM for the Visually Impaired

First Steps towards Stereo-based 6DOF SLAM for the Visually ...

引用

ieee computer society conference on computer vision and pattern recognition Workshops (cvprW)

作者： J. Manuel Saez F. Escolano A. Penalver Robot Vision Group Departmento de Ciencia de la Computacion e Inteligencia Artificial Universidad de Alicante Alicante Spain

In this work we have embodied a full 6DOF SLAM solution into a wearable stereo device working in near realtime. In order to serve on-line metric (map) and positional (localization) to the blind or visually impaired we introduce three basic elements: (i) a real-time egomotion estimation integrating 3D and 2D (appearance) information; (ii) a randomized algorithm for global rectification by entropy minimization; and (iii) a time-dependent view integration strategy for reducing the number of variables and achieving near real-time performance in global rectification. We show successful experimental results which illustrate the capabilities of both the egomotion and the global-rectification algorithm in terms of supporting 6DOF, yielding statistical robustness and enabling scalable solutions.

关键词： Simultaneous localization and mapping computer vision Iterative algorithms Robot vision systems Stereo vision Wearable computers Cameras Personal digital assistants Entropy Minimization methods

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Video-based Human Action *** with Ambiguous Correspondences

引用

ieee computer society conference on computer vision and pattern recognition Workshops (cvprW)

作者： Zhou Feng Tat-Jen Cham School of Computer Engineering Nanyang Technological University Singapore

This paper describes a combined tracking-classification framework for the unsupervised classification of human action. While most existing approaches assume that featurewise correspondences on people are either available or not at all, this method explicitly formalizes how the probability of correspondences can be used in computation when the correspondences are ambiguous. It is also able to exploit in a probabilistic manner any foreground-background preprocessed segmentation, even if the segmentation is of low confidence. A principled analysis of the problem leads to a novel probabilistic action representation called the correspondence-ambiguous feature histogram array (CAFHA) that is robust to variations across similar actions. Our results show that the new framework outperforms the recent Zelnik-Manor and Irani method [19] for unsupervised event classi?cation. Additionally, the framework is extended to quasi real-time action inference, achieving good recognition accuracy despite changes in person identity and variations in the actions.

关键词： Humans Unsupervised learning Biological system modeling State estimation Video sequences Kinematics Motion analysis Feature extraction pattern recognition computer errors

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Localization Based on Building recognition

Localization Based on Building Recognition

引用

ieee computer society conference on computer vision and pattern recognition Workshops (cvprW)

作者： Wei Zhang J. Kosecka Department of Computer Science and Information Technology George Mason University Fairfax VA USA

Navigational capabilities of people in urban areas are to a large extent determined by their knowledge of current location. In addition to location information available by means of global positioning sensors, images can provide additional and often complementary information about relative position and/or viewpoint of the person with respect to some known landmarks. In order to enable such functionality, landmarks (e.g. buildings) have to be reliably and efficiently recognized. In this paper we describe a hierarchical approach for recognition of buildings. At the first stage, we use a novel and efficient representation named localized color histograms. This representation enables efficient retrieval of a small number of candidate matches from the database. At the second stage, the recognition is refined by matching descriptors associated with local image regions. Once the correct building is identified, the relative pose with respect to the building is recovered. The proposed approach is validated by extensive experiments, with images taken in different weather conditions, seasons and with different cameras.

关键词： Navigation Image databases Digital cameras Hidden Markov models Urban areas Image sensors Histograms Object recognition Scalability Layout

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Histograms of oriented gradients for human detection

Histograms of oriented gradients for human detection

引用

conference on computer vision and pattern recognition (cvpr)

作者： N. Dalal B. Triggs INRIA Rhône-Alpes Montbonnot France

We study the question of feature sets for robust visual object recognition; adopting linear SVM based human detection as a test case. After reviewing existing edge and gradient based descriptors, we show experimentally that grids of histograms of oriented gradient (HOG) descriptors significantly outperform existing feature sets for human detection. We study the influence of each stage of the computation on performance, concluding that fine-scale gradients, fine orientation binning, relatively coarse spatial binning, and high-quality local contrast normalization in overlapping descriptor blocks are all important for good results. The new approach gives near-perfect separation on the original MIT pedestrian database, so we introduce a more challenging dataset containing over 1800 annotated human images with a large range of pose variations and backgrounds.

关键词： Histograms Humans Robustness Object recognition Support vector machines Object detection Testing Image edge detection High performance computing Image databases

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Scene-adapted structured light

Scene-adapted structured light

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conference on computer vision and pattern recognition (cvpr)

作者： T.P. Koninckx P. Peers P. Dutre L. Van Gool Department ESAT / VISICS Katholieke Universiteit Leuven Leuven Belgium Department Computer Science Katholieke Universiteit Leuven Leuven Belgium D-ITET / BIWI Swiss Federal Institute of Technology Zurich Switzerland

In order to overcome several limitations of structured light 3D acquisition methods, the colors, intensities, and shapes of the projected patterns are adapted to the scene. Based on a crude estimate of the scene geometry and reflectance characteristics, the local intensity ranges in the projected patterns are adapted, in order to avoid over- and under-exposure in the image. This avoids the infamous specularity problems and generally increases accuracy. The estimated geometry also helps to limit the effect of aliasing caused by the sampling of foreshortened patterns. Furthermore, the approach also accounts for the adverse effects that small motions during scanning would normally have. Moreover, the approach yields a confidence measure at every pixel of the range image. Last but not least, the scanner consists of consumer products only, and therefore is cheap.

关键词： Layout Geometry Cameras Costs Reflectivity Robustness computer science Shape Image sampling Pixel

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Efficient image matching with distributions of local invariant features

Efficient image matching with distributions of local invaria...

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conference on computer vision and pattern recognition (cvpr)

作者： K. Grauman T. Darrell Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology USA

Sets of local features that are invariant to common image transformations are an effective representation to use when comparing images; current methods typically judge feature sets' similarity via a voting scheme (which ignores co-occurrence statistics) or by comparing histograms over a set of prototypes (which must be found by clustering). We present a method for efficiently comparing images based on their discrete distributions (bags) of distinctive local invariant features, without clustering descriptors. Similarity between images is measured with an approximation of the Earth Mover's Distance (EMD), which quickly computes minimal-cost correspondences between two bags of features. Each image's feature distribution is mapped into a normed space with a low-distortion embedding of EMD. Examples most similar to a novel query image are retrieved in time sublinear in the number of examples via approximate nearest neighbor search in the embedded space. We evaluate our method with scene, object, and texture recognition tasks.

关键词： Image matching Voting Image databases Prototypes Spatial databases Histograms Layout computer vision Image retrieval Frequency

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3D Face recognition using Mapped Depth Images

3D Face Recognition using Mapped Depth Images

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ieee computer society conference on computer vision and pattern recognition Workshops (cvprW)

作者： Gang Pan Shi Han Zhaohui Wu Yueming Wang Department of Computer Science University of Zhejiang Hangzhou China

This paper addresses 3D face recognition from facial shape. Firstly, we present an effective method to automatically extract ROI of facial surface, which mainly depends on automatic detection of facial bilateral symmetry plane and localization of nose tip. Then we build a reference plane through the nose tip for calculating the relative depth values. Considering the non-rigid property of facial surface, the ROI is triangulated and parameterized into an isomorphic 2D planar circle, attempting to preserve the intrinsic geometric properties. At the same time the relative depth values are also mapped. Finally we perform eigenface on the mapped relative depth image. The entire scheme is insensitive to pose variance. The experiment using FRGC database v1.0 obtains the rank-1 identification score of 95%, which outperforms the result of the PCA base-line method by 4%, which demonstrates the effectiveness of our algorithm.

关键词： Face recognition Shape Nose Face detection Humans Robustness computer science Image databases Spatial databases Principal component analysis

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Learning spatiotemporal T-junctions for occlusion detection

Learning spatiotemporal T-junctions for occlusion detection

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conference on computer vision and pattern recognition (cvpr)

作者： N. Apostoloff A. Fitzgibbon Robotics Research Group University of Oxford Oxford UK

The goal of motion segmentation and layer extraction can be viewed as the detection and localization of occluding surfaces. A feature that has been shown to be a particularly strong indicator of occlusion, in both computer vision and neuroscience, is the T-junction; however, little progress has been made in T-junction detection. One reason for this is the difficulty in distinguishing false T-junctions (i.e. those not on an occluding edge) and real T-junctions in cluttered images. In addition to this, their photometric profile alone is not enough for reliable detection. This paper overcomes the first problem by searching for T-junctions not in space, but in space-time. This removes many false T-junctions and creates a simpler image structure to explore. The second problem is mitigated by learning the appearance of T-junctions in these spatiotemporal images. An RVM T-junction classifier is learnt from hand-labelled data using SIFT to capture its redundancy. This detector is then demonstrated in a novel occlusion detector that fuses Canny edges and T-junctions in the spatiotemporal domain to detect occluding edges in the spatial domain.

关键词： Spatiotemporal phenomena Image edge detection computer vision Detectors Motion segmentation Motion detection Neuroscience Photometry Redundancy Fuses

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A discriminative framework for modelling object classes

A discriminative framework for modelling object classes

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conference on computer vision and pattern recognition (cvpr)

作者： A. Holub P. Perona Computation and Neural Systems California Institute of Technology Pasadena CA USA

Here we explore a discriminative learning method on underlying generative models for the purpose of discriminating between object categories. Visual recognition algorithms learn models from a set of training examples. Generative models learn their representations by considering data from a single class. Generative models are popular in computer vision for many reasons, including their ability to elegantly incorporate prior knowledge and to handle correspondences between object parts and detected features. However, generative models are often inferior to discriminative models during classification tasks. We study a discriminative approach to learning object categories which maintains the representational power of generative learning, but trains the generative models in a discriminative manner. The discriminatively trained models perform better during classification tasks as a result of selecting discriminative sets of features. We conclude by proposing a multi-class object recognition system which initially trains object classes in a generative manner, identifies subsets of similar classes with high confusion, and finally trains models for these subsets in a discriminative manner to realize gains in classification performance.

关键词： Object recognition computer vision Object detection Humans Motorcycles Learning systems Power generation Power system modeling Face detection Performance gain

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