mtybilly/Instruct2DS huggingface dataset:
Informed Haar-like Features Improve Pedestrian Detection
Authors: Christian Bauckhage, Armin B. Cremers Shanshan Zhang
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014 (Main Conference Track), 2014

Abstract: We propose a simple yet effective detector for pedestrian detection. The basic idea is to incorporate common sense and everyday knowledge into the design of simple and computationally efficient features. As pedestrians usually appear up-right in image or video data, the problem of pedestrian detection is considerably simpler than general purpose people detection. We therefore employ a statistical model of the up-right human body where the head, the upper body, and the lower body are treated as three distinct components. Our main contribution is to systematically design a pool of rectangular templates that are tailored to this shape model. As we incorporate different kinds of low-level measurements, the resulting multi-modal & multi-channel Haar-like features represent characteristic differences between parts of the human body yet are robust against variations in clothing or environmental settings. Our approach avoids exhaustive searches over all possible configurations of rectangle features and neither relies on random sampling. It thus marks a middle ground among recently published techniques and yields efficient low-dimensional yet highly discriminative features. Experimental results on the INRIA and Caltech pedestrian datasets show that our detector reaches state-of-the-art performance at low computational costs and that our features are robust against occlusions.
Image Reconstruction from Bag-of-Visual-Words
Authors: Tatsuya Harada Hiroharu Kato
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014 (Main Conference Track), 2014

Abstract: The objective of this study is to reconstruct images from Bag-of-Visual-Words (BoVW), which is the de facto standard feature for image retrieval and recognition. BoVW is defined here as a histogram of quantized descriptors extracted densely on a regular grid at a single scale. Despite its wide use, no report describes reconstruction of the original image of a BoVW. This task is challenging for two reasons: 1) BoVW includes quantization errors when local descriptors are assigned to visual words. 2) BoVW lacks spatial information of local descriptors when we count the occurrence of visual words. To tackle this difficult task, we use a large-scale image database to estimate the spatial arrangement of local descriptors. Then this task creates a jigsaw puzzle problem with adjacency and global location costs of visual words. Solving this optimization problem is also challenging because it is known as an NP-Hard problem. We propose a heuristic but efficient method to optimize it. To underscore the effectiveness of our method, we apply it to BoVWs extracted from about 100 different categories and demonstrate that it can reconstruct the original images, although the image features lack spatial information and include quantization errors.
Beta Process Multiple Kernel Learning
Authors: Teng Li, Pierre Moulin Bingbing Ni
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014 (Main Conference Track), 2014

Abstract: In kernel based learning, the kernel trick transforms the original representation of a feature instance into a vector of similarities with the training feature instances, known as kernel representation. However, feature instances are sometimes ambiguous and the kernel representation calculated based on them do not possess any discriminative information, which can eventually harm the trained classifier. To address this issue, we propose to automatically select good feature instances when calculating the kernel representation in multiple kernel learning. Specifically, for the kernel representation calculated for each input feature instance, we multiply it element-wise with a latent binary vector named as instance selection variables, which targets at selecting good instances and attenuate the effect of ambiguous ones in the resulting new kernel representation. Beta process is employed for generating the prior distribution for the latent instance selection variables. We then propose a Bayesian graphical model which integrates both MKL learning and inference for the distribution of the latent instance selection variables. Variational inference is derived for model learning under a max-margin principle. Our method is called Beta process multiple kernel learning. Extensive experiments demonstrate the effectiveness of our method on instance selection and its high discriminative capability for various classification problems in vision.
Random Laplace Feature Maps for Semigroup Kernels on Histograms
Authors: Vikas Sindhwani, Quanfu Fan, Haim Avron, Michael W. Mahoney Jiyan Yang
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014 (Main Conference Track), 2014

Abstract: With the goal of accelerating the training and testing complexity of nonlinear kernel methods, several recent papers have proposed explicit embeddings of the input data into low-dimensional feature spaces, where fast linear methods can instead be used to generate approximate solutions. Analogous to random Fourier feature maps to approximate shift-invariant kernels, such as the Gaussian kernel, we develop a new randomized technique called random Laplace features, to approximate a family of kernel functions adapted to the semigroup structure. This is the natural algebraic structure on the set of histograms and other non-negative data representations. We provide theoretical results on the uniform convergence of random Laplace features. Empirical analyses on image classification and surveillance event detection tasks demonstrate the attractiveness of using random Laplace features relative to several other feature maps proposed in the literature.
Hash-SVM: Scalable Kernel Machines for Large-Scale Visual Classification
Authors: Gang Hua, Wei Fan, Shih-Fu Chang Yadong Mu
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014 (Main Conference Track), 2014

Abstract: This paper presents a novel algorithm which uses compact hash bits to greatly improve the efficiency of non-linear kernel SVM in very large scale visual classification problems. Our key idea is to represent each sample with compact hash bits, over which an inner product is defined to serve as the surrogate of the original nonlinear kernels. Then the problem of solving the nonlinear SVM can be transformed into solving a linear SVM over the hash bits. The proposed Hash-SVM enjoys dramatic storage cost reduction owing to the compact binary representation, as well as a (sub-)linear training complexity via linear SVM. As a critical component of Hash-SVM, we propose a novel hashing scheme for arbitrary non-linear kernels via random subspace projection in reproducing kernel Hilbert space. Our comprehensive analysis reveals a well behaved theoretic bound of the deviation between the proposed hashing-based kernel approximation and the original kernel function. We also derive requirements on the hash bits for achieving a satisfactory accuracy level. Several experiments on large-scale visual classification benchmarks are conducted, including one with over 1 million images. The results show that Hash-SVM greatly reduces the computational complexity (more than ten times faster in many cases) while keeping comparable accuracies.
Transitive Distance Clustering with K-Means Duality
Authors: Chunjing Xu, Deyu Meng, Zhuo Hui, Fanyi Xiao, Wenbo Liu, Jianzhuang Liu Zhiding Yu
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014 (Main Conference Track), 2014

Abstract: We propose a very intuitive and simple approximation for the conventional spectral clustering methods. It effectively alleviates the computational burden of spectral clustering - reducing the time complexity from O(n^3) to O(n^2) - while capable of gaining better performance in our experiments. Specifically, by involving a more realistic and effective distance and the "k-means duality" property, our algorithm can handle datasets with complex cluster shapes, multi-scale clusters and noise. We also show its superiority in a series of its real applications on tasks including digit clustering as well as image segmentation.
Simultaneous Twin Kernel Learning using Polynomial Transformations for Structured Prediction
Authors: Ahmed Elgammal Chetan Tonde
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014 (Main Conference Track), 2014

Abstract: Many learning problems in computer vision can be posed as structured prediction problems, where the input and output instances are structured objects such as trees, graphs or strings rather than, single labels {+1, -1} or scalars. Kernel methods such as Structured Support Vector Machines , Twin Gaussian Processes (TGP), Structured Gaussian Processes, and vector-valued Reproducing Kernel Hilbert Spaces (RKHS), offer powerful ways to perform learning and inference over these domains. Positive definite kernel functions allow us to quantitatively capture similarity between a pair of instances over these arbitrary domains. A poor choice of the kernel function, which decides the RKHS feature space, often results in poor performance. Automatic kernel selection methods have been developed, but have focused only on kernels on the input domain (i.e.'one-way'). In this work, we propose a novel and efficient algorithm for learning kernel functions simultaneously, on both input and output domains. We introduce the idea of learning polynomial kernel transformations, and call this method Simultaneous Twin Kernel Learning (STKL). STKL can learn arbitrary, but continuous kernel functions, including 'one-way' kernel learning as a special case. We formulate this problem for learning covariances kernels of Twin Gaussian Processes. Our experimental evaluation using learned kernels on synthetic and several real-world datasets demonstrate consistent improvement in performance of TGP's.
Bregman Divergences for Infinite Dimensional Covariance Matrices
Authors: Mathieu Salzmann, Fatih Porikli Mehrtash Harandi
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014 (Main Conference Track), 2014

Abstract: We introduce an approach to computing and comparing Covariance Descriptors (CovDs) in infinite-dimensional spaces. CovDs have become increasingly popular to address classification problems in computer vision. While CovDs offer some robustness to measurement variations, they also throw away part of the information contained in the original data by only retaining the second-order statistics over the measurements. Here, we propose to overcome this limitation by first mapping the original data to a high-dimensional Hilbert space, and only then compute the CovDs. We show that several Bregman divergences can be computed between the resulting CovDs in Hilbert space via the use of kernels. We then exploit these divergences for classification purpose. Our experiments demonstrate the benefits of our approach on several tasks, such as material and texture recognition, person re-identification, and action recognition from motion capture data.
Optimizing Average Precision using Weakly Supervised Data
Authors: C. V. Jawahar, M. Pawan Kumar Aseem Behl
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014 (Main Conference Track), 2014

Abstract: The performance of binary classification tasks, such as action classification and object detection, is often measured in terms of the average precision (AP). Yet it is common practice in computer vision to employ the support vector machine (SVM) classifier, which optimizes a surrogate 0-1 loss. The popularity of SVM can be attributed to its empirical performance. Specifically, in fully supervised settings, SVM tends to provide similar accuracy to the AP-SVM classifier, which directly optimizes an AP-based loss. However, we hypothesize that in the significantly more challenging and practically useful setting of weakly supervised learning, it becomes crucial to optimize the right accuracy measure. In order to test this hypothesis, we propose a novel latent AP-SVM that minimizes a carefully designed upper bound on the AP-based loss function over weakly supervised samples. Using publicly available datasets, we demonstrate the advantage of our approach over standard loss-based binary classifiers on two challenging problems: action classification and character recognition.
Subspace Clustering for Sequential Data
Authors: Junbin Gao, Yi Guo Stephen Tierney
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014 (Main Conference Track), 2014

Abstract: We propose Ordered Subspace Clustering (OSC) to segment data drawn from a sequentially ordered union of subspaces. Current subspace clustering techniques learn the relationships within a set of data and then use a separate clustering algorithm such as NCut for final segmentation. In contrast our technique, under certain conditions, is capable of segmenting clusters intrinsically without providing the number of clusters as a parameter. Similar to Sparse Subspace Clustering (SSC) we formulate the problem as one of finding a sparse representation but include a new penalty term to take care of sequential data. We test our method on data drawn from infrared hyper spectral data, video sequences and face images. Our experiments show that our method, OSC, outperforms the state of the art methods: Spatial Subspace Clustering (SpatSC), Low-Rank Representation (LRR) and SSC.
Predicting Multiple Attributes via Relative Multi-task Learning
Authors: Qiang Zhang, Baoxin Li Lin Chen
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014 (Main Conference Track), 2014

Abstract: Relative attributes learning aims to learn ranking functions describing the relative strength of attributes. Most of current learning approaches learn ranking functions for each attribute independently without considering possible intrinsic relatedness among the attributes. For a problem involving multiple attributes, it is reasonable to assume that utilizing such relatedness among the attributes would benefit learning, especially when the number of labeled training pairs are very limited. In this paper, we proposed a relative multi-attribute learning framework that integrates relative attributes into a multi-task learning scheme. The formulation allows us to exploit the advantages of the state-of-the-art regularization-based multi-task learning for improved attribute learning. In particular, using joint feature learning as the case studies, we evaluated our framework with both synthetic data and two real datasets. Experimental results suggest that the proposed framework has clear performance gain in ranking accuracy and zero-shot learning accuracy over existing methods of independent relative attributes learning and multi-task learning.
Learning Inhomogeneous FRAME Models for Object Patterns
Authors: Wenze Hu, Song-Chun Zhu, Ying Nian Wu Jianwen Xie
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014 (Main Conference Track), 2014

Abstract: We investigate an inhomogeneous version of the FRAME (Filters, Random field, And Maximum Entropy) model and apply it to modeling object patterns. The inhomogeneous FRAME is a non-stationary Markov random field model that reproduces the observed marginal distributions or statistics of filter responses at all the different locations, scales and orientations. Our experiments show that the inhomogeneous FRAME model is capable of generating a wide variety of object patterns in natural images. We then propose a sparsified version of the inhomogeneous FRAME model where the model reproduces observed statistical properties of filter responses at a small number of selected locations, scales and orientations. We propose to select these locations, scales and orientations by a shared sparse coding scheme, and we explore the connection between the sparse FRAME model and the linear additive sparse coding model. Our experiments show that it is possible to learn sparse FRAME models in unsupervised fashion and the learned models are useful for object classification.
Empirical Minimum Bayes Risk Prediction: How to Extract an Extra Few % Performance from Vision Models with Just Three More Parameters
Authors: Daniel Tarlow, Dhruv Batra Vittal Premachandran
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014 (Main Conference Track), 2014

Abstract: When building vision systems that predict structured objects such as image segmentations or human poses, a crucial concern is performance under task-specific evaluation measures (e.g. Jaccard Index or Average Precision). An ongoing research challenge is to optimize predictions so as to maximize performance on such complex measures. In this work, we present a simple meta-algorithm that is surprisingly effective – Empirical Min Bayes Risk. EMBR takes as input a pre-trained model that would normally be the final product and learns three additional parameters so as to optimize performance on the complex high-order task-specific measure. We demonstrate EMBR in several domains, taking existing state-of-the-art algorithms and improving performance up to ~7%, simply with three extra parameters.
Fantope Regularization in Metric Learning
Authors: Nicolas Thome, Matthieu Cord Marc T. Law
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014 (Main Conference Track), 2014

Abstract: This paper introduces a regularization method to explicitly control the rank of a learned symmetric positive semidefinite distance matrix in distance metric learning. To this end, we propose to incorporate in the objective function a linear regularization term that minimizes the k smallest eigenvalues of the distance matrix. It is equivalent to minimizing the trace of the product of the distance matrix with a matrix in the convex hull of rank-k projection matrices, called a Fantope. Based on this new regularization method, we derive an optimization scheme to efficiently learn the distance matrix. We demonstrate the effectiveness of the method on synthetic and challenging real datasets of face verification and image classification with relative attributes, on which our method outperforms state-of-the-art metric learning algorithms.
Kernel-PCA Analysis of Surface Normals for Shape-from-Shading
Authors: Stefanos Zafeiriou Patrick Snape
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014 (Main Conference Track), 2014

Abstract: We propose a kernel-based framework for computing components from a set of surface normals. This framework allows us to easily demonstrate that component analysis can be performed directly upon normals. We link previously proposed mapping functions, the azimuthal equidistant projection (AEP) and principal geodesic analysis (PGA), to our kernel-based framework. We also propose a new mapping function based upon the cosine distance between normals. We demonstrate the robustness of our proposed kernel when trained with noisy training sets. We also compare our kernels within an existing shape-from-shading (SFS) algorithm. Our spherical representation of normals, when combined with the robust properties of cosine kernel, produces a very robust subspace analysis technique. In particular, our results within SFS show a substantial qualitative and quantitative improvement over existing techniques.
Merging SVMs with Linear Discriminant Analysis: A Combined Model
Authors: Stefanos Zafeiriou, Maja Pantic Symeon Nikitidis
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014 (Main Conference Track), 2014

Abstract: A key problem often encountered by many learning algorithms in computer vision dealing with high dimensional data is the so called "curse of dimensionality" which arises when the available training samples are less than the input feature space dimensionality. To remedy this problem, we propose a joint dimensionality reduction and classification framework by formulating an optimization problem within the maximum margin class separation task. The proposed optimization problem is solved using alternative optimization where we jointly compute the low dimensional maximum margin projections and the separating hyperplanes in the projection subspace. Moreover, in order to reduce the computational cost of the developed optimization algorithm we incorporate orthogonality constraints on the derived projection bases and show that the resulting combined model is an alternation between identifying the optimal separating hyperplanes and performing a linear discriminant analysis on the support vectors. Experiments on face, facial expression and object recognition validate the effectiveness of the proposed method against state-of-the-art dimensionality reduction algorithms.
Stable Learning in Coding Space for Multi-Class Decoding and Its Extension for Multi-Class Hypothesis Transfer Learning
Authors: Yi Wang, Yang Wang, Fang Chen Bang Zhang
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014 (Main Conference Track), 2014

Abstract: Many prevalent multi-class classification approaches can be unified and generalized by the output coding framework which usually consists of three phases: (1) coding, (2) learning binary classifiers, and (3) decoding. Most of these approaches focus on the first two phases and predefined distance function is used for decoding. In this paper, however, we propose to perform learning in coding space for more adaptive decoding, thereby improving overall performance. Ramp loss is exploited for measuring multi-class decoding error. The proposed algorithm has uniform stability. It is insensitive to data noises and scalable with large scale datasets. Generalization error bound and numerical results are given with promising outcomes.
Finding the Subspace Mean or Median to Fit Your Need
Authors: J. Ross Beveridge, Bruce Draper, Michael Kirby, Chris Peterson Tim Marrinan
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014 (Main Conference Track), 2014

Abstract: Many computer vision algorithms employ subspace models to represent data. Many of these approaches benefit from the ability to create an average or prototype for a set of subspaces. The most popular method in these situations is the Karcher mean, also known as the Riemannian center of mass. The prevalence of the Karcher mean may lead some to assume that it provides the best average in all scenarios. However, other subspace averages that appear less frequently in the literature may be more appropriate for certain tasks. The extrinsic manifold mean, the L2-median, and the flag mean are alternative averages that can be substituted directly for the Karcher mean in many applications.  This paper evaluates the characteristics and performance of these four averages on synthetic and real-world data. While the Karcher mean generalizes the Euclidean mean to the Grassman manifold, we show that the extrinsic manifold mean, the L2-median, and the flag mean behave more like medians and are therefore more robust to the presence of outliers among the subspaces being averaged. We also show that while the Karcher mean and L2-median are computed using iterative algorithms, the extrinsic manifold mean and flag mean can be found analytically and are thus orders of magnitude faster in practice. Finally, we show that the flag mean is a generalization of the extrinsic manifold mean that permits subspaces with different numbers of dimensions to be averaged. The result is a "cookbook" that maps algorithm constraints and data properties to the most appropriate subspace mean for a given application.
Adaptive Color Attributes for Real-Time Visual Tracking
Authors: Fahad Shahbaz Khan, Michael Felsberg, Joost van de Weijer Martin Danelljan
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014 (Main Conference Track), 2014

Abstract: Visual tracking is a challenging problem in computer vision. Most state-of-the-art visual trackers either rely on luminance information or use simple color representations for image description. Contrary to visual tracking, for object recognition and detection, sophisticated color features when combined with luminance have shown to provide excellent performance. Due to the complexity of the tracking problem, the desired color feature should be computationally efficient, and possess a certain amount of photometric invariance while maintaining high discriminative power.  This paper investigates the contribution of color in a tracking-by-detection framework. Our results suggest that color attributes provides superior performance for visual tracking. We further propose an adaptive low-dimensional variant of color attributes. Both quantitative and attribute-based evaluations are performed on 41 challenging benchmark color sequences. The proposed approach improves the baseline intensity-based tracker by 24 % in median distance precision. Furthermore, we show that our approach outperforms state-of-the-art tracking methods while running at more than 100 frames per second.
Local Layering for Joint Motion Estimation and Occlusion Detection
Authors: Ce Liu, Hanspeter Pfister Deqing Sun
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014 (Main Conference Track), 2014

Abstract: Most motion estimation algorithms (optical flow, layered models) cannot handle large amount of occlusion in textureless regions, as motion is often initialized with no occlusion assumption despite that occlusion may be included in the final objective. To handle such situations, we propose a local layering model where motion and occlusion relationships are inferred jointly. In particular, the uncertainties of occlusion relationships are retained so that motion is inferred by considering all the possibilities of local occlusion relationships.  In addition, the local layering model handles articulated objects with self-occlusion. We demonstrate that the local layering model can handle motion and occlusion well for both challenging synthetic and real sequences.
Realtime and Robust Hand Tracking from Depth
Authors: Xiao Sun, Yichen Wei, Xiaoou Tang, Jian Sun Chen Qian
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014 (Main Conference Track), 2014

Abstract: We present a realtime hand tracking system using a depth sensor. It tracks a fully articulated hand under large viewpoints in realtime (25 FPS on a desktop without using a GPU) and with high accuracy (error below 10 mm). To our knowledge, it is the first system that achieves such robustness, accuracy, and speed simultaneously, as verified on challenging real data.  Our system is made of several novel techniques. We model a hand simply using a number of spheres and define a fast cost function. Those are critical for realtime performance. We propose a hybrid method that combines gradient based and stochastic optimization methods to achieve fast convergence and good accuracy. We present new finger detection and hand initialization methods that greatly enhance the robustness of tracking.
Multi-Output Learning for Camera Relocalization
Authors: Pushmeet Kohli, Ben Glocker, Jamie Shotton, Toby Sharp, Andrew Fitzgibbon, Shahram Izadi Abner Guzman-Rivera
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014 (Main Conference Track), 2014

Abstract: We address the problem of estimating the pose of a cam- era relative to a known 3D scene from a single RGB-D frame. We formulate this problem as inversion of the generative rendering procedure, i.e., we want to find the camera pose corresponding to a rendering of the 3D scene model that is most similar with the observed input. This is a non-convex optimization problem with many local optima. We propose a hybrid discriminative-generative learning architecture that consists of: (i) a set of M predictors which generate M camera pose hypotheses; and (ii) a 'selector' or 'aggregator' that infers the best pose from the multiple pose hypotheses based on a similarity function. We are interested in predictors that not only produce good hypotheses but also hypotheses that are different from each other. Thus, we propose and study methods for learning 'marginally relevant' predictors, and compare their performance when used with different selection procedures. We evaluate our method on a recently released 3D reconstruction dataset with challenging camera poses, and scene variability. Experiments show that our method learns to make multiple predictions that are marginally relevant and can effectively select an accurate prediction. Furthermore, our method outperforms the state-of-the-art discriminative approach for camera relocalization.
MAP Visibility Estimation for Large-Scale Dynamic 3D Reconstruction
Authors: Hyun Soo Park, Yaser Sheikh Hanbyul Joo
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014 (Main Conference Track), 2014

Abstract: Many traditional challenges in reconstructing 3D motion, such as matching across wide baselines and handling occlusion, reduce in significance as the number of unique viewpoints increases. However, to obtain this benefit, a new challenge arises: estimating precisely which cameras observe which points at each instant in time. We present a maximum a posteriori (MAP) estimate of the time-varying visibility of the target points to reconstruct the 3D motion of an event from a large number of cameras. Our algorithm takes, as input, camera poses and image sequences, and outputs the time-varying set of the cameras in which a target patch is visibile and its reconstructed trajectory. We model visibility estimation as a MAP estimate by incorporating various cues including photometric consistency, motion consistency, and geometric consistency, in conjunction with a prior that rewards consistent visibilities in proximal cameras. An optimal estimate of visibility is obtained by finding the minimum cut of a capacitated graph over cameras. We demonstrate that our method estimates visibility with greater accuracy, and increases tracking performance producing longer trajectories, at more locations, and at higher accuracies than methods that ignore visibility or use photometric consistency alone.
Multi-Object Tracking via Constrained Sequential Labeling
Authors: Alan Fern, Sinisa Todorovic Sheng Chen
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014 (Main Conference Track), 2014

Abstract: This paper presents a new approach to tracking people in crowded scenes, where people are subject to long-term (partial) occlusions and may assume varying postures and articulations. In such videos, detection-based trackers give poor performance since detecting people occurrences is not reliable, and common assumptions about locally smooth trajectories do not hold. Rather, we use temporal mid-level features (e.g., supervoxels or dense point trajectories) as a more coherent spatiotemporal basis for handling occlusion and pose variations.Thus, we formulate tracking as labeling mid-level features by object identifiers, and specify a new approach, called constrained sequential labeling (CSL), for performing this labeling. CSL uses a cost function to sequentially assign labels while respecting the implications of hard constraints computed via constraint propagation. A key feature of this approach is that it allows for the use of flexible cost functions and constraints that capture complex dependencies that cannot be represented in standard network-flow formulations. To exploit this flexibility we describe how to learn constraints and give a provably correct learning algorithms for cost functions that achieves finitetime convergence at a rate that improves with the strength of the constraints. Our experimental results indicate that CSL outperforms the state-of-the-art on challenging real-world videos of volleyball, basketball, and pedestrians walking.
A Primal-Dual Algorithm for Higher-Order Multilabel Markov Random Fields
Authors: Chen Wang, Ramin Zabih Alexander Fix
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014 (Main Conference Track), 2014

Abstract: Graph cuts method such as a-expansion [4] and fusion moves [22] have been successful at solving many optimization problems in computer vision. Higher-order Markov Random Fields (MRF's), which are important for numerous applications, have proven to be very difficult, especially for multilabel MRF's (i.e. more than 2 labels). In this paper we propose a new primal-dual energy minimization method for arbitrary higher-order multilabel MRF's. Primal-dual methods provide guaranteed approximation bounds, and can exploit information in the dual variables to improve their efficiency. Our algorithm generalizes the PD3 [19] technique for first-order MRFs, and relies on a variant of max-flow that can exactly optimize certain higher-order binary MRF's [14]. We provide approximation bounds similar to PD3 [19], and the method is fast in practice. It can optimize non-submodular MRF's, and additionally can in- corporate problem-specific knowledge in the form of fusion proposals. We compare experimentally against the existing approaches that can efficiently handle these difficult energy functions [6, 10, 11]. For higher-order denoising and stereo MRF's, we produce lower energy while running significantly faster.
Energy Based Multi-model Fitting & Matching for 3D Reconstruction
Authors: Yuri Boykov Hossam Isack
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014 (Main Conference Track), 2014

Abstract: Standard geometric model fitting methods take as an input a fixed set of feature pairs greedily matched based only on their appearances. Inadvertently, many valid matches are discarded due to repetitive texture or large baseline between view points. To address this problem, matching should consider both feature appearances and geometric fitting errors. We jointly solve feature matching and multi-model fitting problems by optimizing one energy. The formulation is based on our generalization of the assignment problem and its efficient min-cost-max-flow solver. Our approach significantly increases the number of correctly matched features, improves the accuracy of fitted models, and is robust to larger baselines.
Submodularization for Binary Pairwise Energies
Authors: Yuri Boykov, Olga Veksler, Ismail Ben Ayed, Andrew Delong Lena Gorelick
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014 (Main Conference Track), 2014

Abstract: Many computer vision problems require optimization of binary non-submodular energies. We propose a general optimization framework based on local submodular approximations (LSA). Unlike standard LP relaxation methods that linearize the whole energy globally, our approach iteratively approximates the energies locally. On the other hand, unlike standard local optimization methods (e.g. gradient descent or projection techniques) we use non-linear submodular approximations and optimize them without leaving the domain of integer solutions. We discuss two specific LSA algorithms based on  trust region and auxiliary function principles, LSA-TR and LSA-AUX. These methods obtain state-of-the-art results on a wide range of applications outperforming many standard techniques such as LBP, QPBO, and TRWS. While our paper is focused on pairwise energies, our ideas extend to higher-order problems. The code is available online
Maximum Persistency in Energy Minimization
Authors: Alexander Shekhovtsov
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014 (Main Conference Track), 2014

Abstract: We consider discrete pairwise energy minimization problem (weighted constraint satisfaction, max-sum labeling) and methods that identify a globally optimal partial assignment of variables. When finding a complete optimal assignment is intractable, determining optimal values for a part of variables is an interesting possibility. Existing methods are based on different sufficient conditions.  We propose a new sufficient condition for partial optimality which is: (1) verifiable in polynomial time (2) invariant to reparametrization of the problem and permutation of labels and (3) includes many existing sufficient conditions as special cases. We pose the problem of finding the maximum optimal partial assignment identifiable by the new sufficient condition. A polynomial method is proposed which is guaranteed to assign same or larger part of variables. The core of the method is a specially constructed linear program that identifies persistent assignments in an arbitrary multi-label setting.
Partial Optimality by Pruning for MAP-inference with General Graphical Models
Authors: Bogdan Savchynskyy, Jorg H. Kappes, Christoph Schnorr Paul Swoboda
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014 (Main Conference Track), 2014

Abstract: We consider the energy minimization problem for undirected graphical models, also known as MAP-inference problem for Markov random fields which is NP-hard in general. We propose a novel polynomial time algorithm to obtain a part of its optimal nonrelaxed integral solution. Our algorithm is initialized with variables taking integral values in the solution of a convex relaxation of the MAP-inference problem and iteratively prunes those, which do not satisfy our criterion for partial optimality. We show that our pruning strategy is in a certain sense theoretically optimal. Also empirically our method outperforms previous approaches in terms of the number of persistently labelled variables. The method is very general, as it is applicable to models with arbitrary factors of an arbitrary order and can employ any solver for the considered relaxed problem. Our method's runtime is determined by the runtime of the convex relaxation solver for the MAP-inference problem.
Scene Labeling Using Beam Search Under Mutex Constraints
Authors: Sinisa Todorovic Anirban Roy
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014 (Main Conference Track), 2014

Abstract: This paper addresses the problem of assigning object class labels to image pixels. Following recent holistic formulations, we cast scene labeling as inference of a conditional random field (CRF) grounded onto superpixels. The CRF inference is specified as quadratic program (QP) with mutual exclusion (mutex) constraints on class label assignments. The QP is solved using a beam search (BS), which is well-suited for scene labeling, because it explicitly accounts for spatial extents of objects; conforms to inconsistency constraints from domain knowledge; and has low computational costs. BS gradually builds a search tree whose nodes correspond to candidate scene labelings. Successor nodes are repeatedly generated from a select set of their parent nodes until convergence. We prove that our BS efficiently maximizes the QP objective of CRF inference. Effectiveness of our BS for scene labeling is evaluated on the benchmark MSRC, Stanford Backgroud, PASCAL VOC 2009 and 2010 datasets.
Persistent Tracking for Wide Area Aerial Surveillance
Authors: Gerard Medioni Jan Prokaj
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014 (Main Conference Track), 2014

Abstract: Persistent surveillance of large geographic areas from unmanned aerial vehicles allows us to learn much about the daily activities in the region of interest. Nearly all of the approaches addressing tracking in this imagery are detection-based and rely on background subtraction or frame differencing to provide detections. This, however, makes it difficult to track targets once they slow down or stop, which is not acceptable for persistent tracking, our goal.  We present a multiple target tracking approach that does not exclusively rely on background subtraction and is better able to track targets through stops. It accomplishes this by effectively running two trackers in parallel: one based on detections from background subtraction providing target initialization and reacquisition, and one based on a target state regressor providing frame to frame tracking. We evaluated the proposed approach on a long sequence from a wide area aerial imagery dataset, and the results show improved object detection rates and ID-switch rates with limited increases in false alarms compared to the competition.
Multi-Cue Visual Tracking Using Robust Feature-Level Fusion Based on Joint Sparse Representation
Authors: Andy J. Ma, Pong C. Yuen Xiangyuan Lan
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014 (Main Conference Track), 2014

Abstract: The use of multiple features for tracking has been proved as an effective approach because limitation of each feature could be compensated. Since different types of variations such as illumination, occlusion and pose may happen in a video sequence, especially long sequence videos, how to dynamically select the appropriate features is one of the key problems in this approach. To address this issue in multicue visual tracking, this paper proposes a new joint sparse representation model for robust feature-level fusion. The proposed method dynamically removes unreliable features to be fused for tracking by using the advantages of sparse representation. As a result, robust tracking performance is obtained. Experimental results on publicly available videos show that the proposed method outperforms both existing sparse representation based and fusion-based trackers.
Multi-Forest Tracker: A Chameleon in Tracking
Authors: Slobodan Ilic David J. Tan
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014 (Main Conference Track), 2014

Abstract: In this paper, we address the problem of object tracking in intensity images and depth data. We propose a generic framework that can be used either for tracking 2D templates in intensity images or for tracking 3D objects in depth images. To overcome problems like partial occlusions, strong illumination changes and motion blur, that notoriously make energy minimization-based tracking methods get trapped in a local minimum, we propose a learning-based method that is robust to all these problems. We use random forests to learn the relation between the parameters that defines the object's motion, and the changes they induce on the image intensities or the point cloud of the template. It follows that, to track the template when it moves, we use the changes on the image intensities or point cloud to predict the parameters of this motion. Our algorithm has an extremely fast tracking performance running at less than 2 ms per frame, and is robust to partial occlusions. Moreover, it demonstrates robustness to strong illumination changes when tracking templates using intensity images, and robustness in tracking 3D objects from arbitrary viewpoints even in the presence of motion blur that causes missing or erroneous data in depth images. Extensive experimental evaluation and comparison to the related approaches strongly demonstrates the benefits of our method.
Rigid Motion Segmentation using Randomized Voting
Authors: Jeongwoo Ju, Junmo Kim Heechul Jung
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014 (Main Conference Track), 2014

Abstract: In this paper, we propose a novel rigid motion segmentation algorithm called randomized voting (RV). This algorithm is based on epipolar geometry, and computes a score using the distance between the feature point and the corresponding epipolar line. This score is accumulated and utilized for final grouping. Our algorithm basically deals with two frames, so it is also applicable to the two-view motion segmentation problem. For evaluation of our algorithm, Hopkins 155 dataset, which is a representative test set for rigid motion segmentation, is adopted; it consists of two and three rigid motions. Our algorithm has provided the most accurate motion segmentation results among all of the state-of-the-art algorithms. The average error rate is 0.77%. In addition, when there is measurement noise, our algorithm is comparable with other state-of-the-art algorithms.
Robust Online Multi-Object Tracking based on Tracklet Confidence and Online Discriminative Appearance Learning
Authors: Kuk-Jin Yoon Seung-Hwan Bae
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014 (Main Conference Track), 2014

Abstract: Online multi-object tracking aims at producing complete tracks of multiple objects using the information accumulated up to the present moment. It still remains a difficult problem in complex scenes, because of frequent occlusion by clutter or other objects, similar appearances of different objects, and other factors. In this paper, we propose a robust online multi-object tracking method that can handle these difficulties effectively. We first propose the tracklet confidence using the detectability and continuity of a tracklet, and formulate a multi-object tracking problem based on the tracklet confidence. The multi-object tracking problem is then solved by associating tracklets in different ways according to their confidence values. Based on this strategy, tracklets sequentially grow with online-provided detections, and fragmented tracklets are linked up with others without any iterative and expensive associations. Here, for reliable association between tracklets and detections, we also propose a novel online learning method using an incremental linear discriminant analysis for discriminating the appearances of objects. By exploiting the proposed learning method, tracklet association can be successfully achieved even under severe occlusion. Experiments with challenging public datasets show distinct performance improvement over other batch and online tracking methods.
Pyramid-based Visual Tracking Using Sparsity Represented Mean Transform
Authors: Kin Hong Wong Zhe Zhang
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014 (Main Conference Track), 2014

Abstract: In this paper, we propose a robust method for visual tracking relying on mean shift, sparse coding and spatial pyramids. Firstly, we extend the original mean shift approach to handle orientation space and scale space and name this new method as mean transform. The mean transform method estimates the motion, including the location, orientation and scale, of the interested object window simultaneously and effectively. Secondly, a pixel-wise dense patch sampling technique and a region-wise trivial template designing scheme are introduced which enable our approach to run very accurately and efficiently. In addition, instead of using either holistic representation or local representation only, we apply spatial pyramids by combining these two representations into our approach to deal with partial occlusion problems robustly. Observed from the experimental results, our approach outperforms state-of-the-art methods in many benchmark sequences.