pwc-archive/papers-with-abstracts huggingface dataset:
Robust Online Multi-Object Tracking based on Tracklet Confidence and Online Discriminative Appearance Learning
Authors: Seung-Hwan Bae, Kuk-Jin Yoon
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: Multi-Object Tracking, Object, Object Tracking, Online Multi-Object Tracking

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: Zhe Zhang, Kin Hong Wong
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: Visual Tracking

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.
Tracklet Association with Online Target-Specific Metric Learning
Authors: Bing Wang, Gang Wang, Kap Luk Chan, Li Wang
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: Metric Learning

This paper presents a novel introduction of online target-specific metric learning in track fragment (tracklet) association by network flow optimization for long-term multi-person tracking. Different from other network flow formulation, each node in our network represents a tracklet, and each edge represents the likelihood of   neighboring tracklets belonging to the same trajectory as measured by our proposed affinity score.  In our method, target-specific similarity metrics are learned, which give rise to the appearance-based models used in the tracklet affinity estimation. Trajectory-based tracklets are refined by using the learned metrics to account for appearance consistency and to identify reliable tracklets. The metrics are then re-learned using reliable tracklets for computing tracklet affinity scores.  Long-term trajectories are then obtained through network flow optimization. Occlusions and missed detections are handled by a trajectory completion step. Our method is effective for long-term tracking even when the targets are spatially close or completely occluded by others. We validate our proposed framework on several public datasets and show that it outperforms several state of art methods.
An Online Learned Elementary Grouping Model for Multi-target Tracking
Authors: Xiaojing Chen, Zhen Qin, Le An, Bir Bhanu
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6

We introduce an online approach to learn possible elementary groups (groups that contain only two targets) for inferring high level context that can be used to improve multi-target tracking in a data-association based framework. Unlike most existing association-based tracking approaches that use only low level information (e.g., time, appearance, and motion) to build the affinity model and consider each target as an independent agent, we online learn social grouping behavior to provide additional information for producing more robust tracklets affinities. Social grouping behavior of pairwise targets is first learned from confident tracklets and encoded in a disjoint grouping graph. The grouping graph is further completed with the help of group tracking. The proposed method is efficient, handles group merge and split, and can be easily integrated into any basic affinity model. We evaluate our approach on two public datasets, and show significant improvements compared with state-of-the-art methods.
Diversity-Enhanced Condensation Algorithm and Its Application for Robust and Accurate Endoscope Three-Dimensional Motion Tracking
Authors: Xiongbiao Luo, Ying Wan, Xiangjian He, Jie Yang, Kensaku MORI
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: Diversity

The paper proposes a diversity-enhanced condensation algorithm to address the particle impoverishment problem which stochastic  filtering usually suffers from. The particle diversity plays an important role as it affects the performance of filtering. Although the condensation algorithm is widely used in computer vision, it easily gets trapped in local minima due to the particle degeneracy. We introduce a modified evolutionary computing method, adaptive differential evolution, to resolve the particle impoverishment under a proper size of particle population. We apply our proposed method to endoscope tracking for estimating three-dimensional motion of the endoscopic camera. The experimental results demonstrate that our proposed method offers more robust and accurate tracking than previous methods. The current tracking smoothness and error were significantly reduced from (3.7, 4.8) to (2.3 mm, 3.2 mm), which approximates the clinical requirement of 3.0 mm.
Partial Occlusion Handling for Visual Tracking via Robust Part Matching
Authors: Tianzhu Zhang, Kui Jia, Changsheng Xu, Yi Ma, Narendra Ahuja
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: Occlusion Handling, Sparse Learning, Visual Tracking

Part-based visual tracking is advantageous due to its robustness against partial occlusion. However, how to effectively exploit the confidence scores of individual parts to construct a robust tracker is still a challenging problem. In this paper, we address this problem by simultaneously matching parts in each of multiple frames, which is realized by a locality-constrained low-rank sparse learning method that establishes multi-frame part correspondences through optimization of partial permutation matrices. The proposed part matching tracker (PMT) has a number of attractive properties. (1) It exploits the spatial-temporal localityconstrained property for robust part matching. (2) It matches local parts from multiple frames jointly by considering their low-rank and sparse structure information, which can effectively handle part appearance variations due to occlusion or noise. (3) The proposed PMT model has the inbuilt mechanism of leveraging multi-mode target templates, so that the dilemma of template updating when encountering occlusion in tracking can be better handled. This contrasts with existing methods that only do part matching between a pair of frames. We evaluate PMT and compare with 10 popular state-of-the-art methods on challenging benchmarks. Experimental results show that PMT consistently outperform these existing trackers.
Speeding Up Tracking by Ignoring Features
Authors: Lu Zhang, Hamdi Dibeklioglu, Laurens van der Maaten
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: Object

Most modern object trackers combine a motion prior with sliding-window detection, using binary classifiers that predict the presence of the target object based on histogram features. Although the accuracy of such trackers is generally very good, they are often impractical because of their high computational requirements. To resolve this problem, the paper presents a new approach that limits the computational costs of trackers by ignoring features in image regions that --- after inspecting a few features --- are unlikely to contain the target object. To this end, we derive an upper bound on the probability that a location is most likely to contain the target object, and we ignore (features in) locations for which this upper bound is small. We demonstrate the effectiveness of our new approach in experiments with model-free and model-based trackers that use linear models in combination with HOG features. The results of our experiments demonstrate that our approach allows us to reduce the average number of inspected features by up to 90% without affecting the accuracy of the tracker.
Subspace Tracking under Dynamic Dimensionality for Online Background Subtraction
Authors: Matthew Berger, Lee M. Seversky
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6

Long-term modeling of background motion in videos is an important and challenging problem used in numerous applications such as segmentation and event recognition. A major challenge in modeling the background from point trajectories lies in dealing with the variable length duration of trajectories, which can be due to such factors as trajectories entering and leaving the frame or occlusion from different depth layers. This work proposes an online method for background modeling of dynamic point trajectories via tracking of a linear subspace describing the background motion. To cope with variability in trajectory durations, we cast subspace tracking as an instance of subspace estimation under missing data, using a least-absolute deviations formulation to robustly estimate the background in the presence of arbitrary foreground motion. Relative to previous works, our approach is very fast and scales to arbitrarily long videos as our method processes new frames sequentially as they arrive.
Multiple Target Tracking Based on Undirected Hierarchical Relation Hypergraph
Authors: Longyin Wen, Wenbo Li, Junjie Yan, Zhen Lei, Dong Yi, Stan Z. Li
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: Relation

Multi-target tracking is an interesting but challenging task in computer vision field. Most previous data association based methods merely consider the relationships (e.g. appearance and motion pattern similarities) between detections in local limited temporal domain, leading to their difficulties in handling long-term occlusion and distinguishing the spatially close targets with similar appearance in crowded scenes. In this paper, a novel data association approach based on undirected hierarchical relation hypergraph is proposed, which formulates the tracking task as a hierarchical dense neighborhoods searching problem on the dynamically constructed undirected affinity graph. The relationships between different detections across the spatiotemporal domain are considered in a high-order way, which makes the tracker robust to the spatially close targets with similar appearance. Meanwhile, the hierarchical design of the optimization process fuels our tracker to long-term occlusion with more robustness. Extensive experiments on various challenging datasets (i.e. PETS2009 dataset, ParkingLot), including both low and high density sequences, demonstrate that the proposed method performs favorably against the state-of-the-art methods.
Bi-label Propagation for Generic Multiple Object Tracking
Authors: Wenhan Luo, Tae-Kyun Kim, Bjorn Stenger, Xiaowei Zhao, Roberto Cipolla
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: Multiple Object Tracking, Object, Object Tracking

In this paper, we propose a label propagation framework to handle the multiple object tracking (MOT) problem for a generic object type (cf. pedestrian tracking). Given a target object by an initial bounding box, all objects of the same type are localized together with their identities. We treat this as a problem of propagating bi-labels, i.e. a binary class label for detection and individual object labels for tracking. To propagate the class label, we adopt clustered Multiple Task Learning (cMTL) while enforcing spatio-temporal consistency and show that this improves the performance when given limited training data. To track  objects, we propagate labels from trajectories to detections based on affinity using appearance, motion, and context. Experiments on public and challenging new sequences show that the proposed method improves over the current state of the art on this task.
A Probabilistic Framework for Multitarget Tracking with Mutual Occlusions
Authors: Menglong Yang, Yiguang Liu, Longyin Wen, Zhisheng You, Stan Z. Li
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6

Mutual occlusions among targets can cause track loss or target position deviation, because the observation likelihood of an occluded target may vanish even when we have the estimated location of the target. This paper presents a novel probability framework for multitarget tracking with mutual occlusions. The primary contribution of this work is the introduction of a vectorial occlusion variable as part of the solution. The occlusion variable describes occlusion states of the targets. This forms the basis of the proposed probability framework, with the following further contributions: 1) Likelihood: A new observation likelihood model is presented, in which the likelihood of an occluded target is computed by referring to both of the occluded and oc-cluding targets. 2) Priori: Markov random field (MRF) is used to model the occlusion priori such that less likely "circular" or "cascading" types of occlusions have lower priori probabilities. Both the occlusion priori and the motion priori take into consideration the state of occlusion. 3) Optimization: A realtime RJMCMC-based algorithm with a newmove type called "occlusion state update" is presented. Experimental results show that the proposed framework can handle occlusions well, even including long-duration full occlusions, which may cause tracking failures in the traditional methods.
Occlusion Geodesics for Online Multi-Object Tracking
Authors: Horst Possegger, Thomas Mauthner, Peter M. Roth, Horst Bischof
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: motion prediction, Multi-Object Tracking, Object, Object Tracking, Online Multi-Object Tracking

Robust multi-object tracking-by-detection requires the correct assignment of noisy detection results to object trajectories. We address this problem by proposing an online approach based on the observation that object detectors primarily fail if objects are significantly occluded. In contrast to most existing work, we only rely on geometric information to efficiently overcome detection failures.  In particular, we exploit the spatio-temporal evolution of occlusion regions, detector reliability, and target motion prediction to robustly handle missed detections. In combination with a conservative association scheme for visible objects, this allows for real-time tracking of multiple objects from a single static camera, even in complex scenarios. Our evaluations on publicly available multi-object tracking benchmark datasets demonstrate favorable performance compared to the state-of-the-art in online and offline multi-object tracking.
Efficient Nonlinear Markov Models for Human Motion
Authors: Andreas M. Lehrmann, Peter V. Gehler, Sebastian Nowozin
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: Action Recognition, Temporal Action Localization

Dynamic Bayesian networks such as Hidden Markov Models (HMMs) are successfully used as probabilistic models for human motion. The use of hidden variables makes them expressive models, but inference is only approximate and requires procedures such as particle filters or Markov chain Monte Carlo methods. In this work we propose to instead use simple Markov models that only model observed quantities. We retain a highly expressive dynamic model by using interactions that are nonlinear and non-parametric. A presentation of our approach in terms of latent variables shows logarithmic growth for the computation of exact log-likelihoods in the number of latent states. We validate our model on human motion capture data and demonstrate state-of-the-art performance on action recognition and motion completion tasks.
A Compositional Model for Low-Dimensional Image Set Representation
Authors: Hossein Mobahi, Ce Liu, William T. Freeman
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6

Learning a low-dimensional representation of images is useful for various applications in graphics and computer vision. Existing solutions either require manually specified landmarks for corresponding points in the images, or are restricted to specific objects or shape deformations. This paper alleviates these limitations by imposing a specific model for generating images; the nested composition of color, shape, and appearance. We show that each component can be approximated by a low-dimensional subspace when the others are factored out. Our formulation allows for efficient learning and experiments show encouraging results.
A Principled Approach for Coarse-to-Fine MAP Inference
Authors: Christopher Zach
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6

In this work we reconsider labeling problems with (virtually) continuous state spaces, which are of relevance in low level computer vision. In order to cope with such huge state spaces multi-scale methods have been proposed to approximately solve such labeling tasks. Although performing well in many cases, these methods do usually not come with any guarantees on the returned solution. A general and principled approach to solve labeling problems is based on the well-known linear programming relaxation, which appears to be prohibitive for large state spaces at the first glance. We demonstrate that a coarse-to-fine exploration strategy in the label space is able to optimize the LP relaxation for non-trivial problem instances with reasonable run-times and moderate memory requirements.
Fast Approximate Inference in Higher Order MRF-MAP Labeling Problems
Authors: Chetan Arora, Subhashis Banerjee, Prem Kalra, S. N. Maheshwari
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6

Use of higher order clique potentials for modeling inference problems has exploded in last few years. The algorithmic schemes proposed so far do not scale well with increasing clique size, thus limiting their use to cliques of size at most 4 in practice. Generic Cuts (GC) of Arora et al. [9] shows that when potentials are submodular, inference problems can be solved optimally in polynomial time for fixed size cliques. In this paper we report an algorithm called Approximate Cuts (AC) which uses a generalization of the gadget of GC and provides an approximate solution to inference in 2-label MRF-MAP problems with cliques of size k ≥ 2. The algorithm gives optimal solution for submodular potentials. When potentials are non-submodular, we show that important properties such as weak persistency hold for solution inferred by AC. AC is a polynomial time primal dual approximation algorithm for fixed clique size. We show experimentally that AC not only provides significantly better solutions in practice, it is an order of magnitude faster than message passing schemes like Dual Decomposition [19] and GTRWS [17] or Reduction based techniques like [10, 13, 14].
Multi Label Generic Cuts: Optimal Inference in Multi Label Multi Clique MRF-MAP Problems
Authors: Chetan Arora, S. N. Maheshwari
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6

We propose an algorithm called Multi Label Generic Cuts (MLGC) for computing optimal solutions to MRF-MAP problems with submodular multi label multi-clique potentials. A transformation is introduced to convert a m-label k-clique problem to an equivalent 2-label (mk)-clique problem. We show that if the original multi-label problem is submodular then the transformed 2-label multi-clique problem is also submodular. We exploit sparseness in the feasible configurations of the transformed 2-label problem to suggest an improvement to Generic Cuts [3] to solve the 2-label problems efficiently. The algorithm runs in time O(m^k n^3 ) in the worst case (n is the number of pixels) generalizing O(2^k n^3) running time of Generic Cuts. We show experimentally that MLGC is an order of magnitude faster than the current state of the art [17, 20]. While the result of MLGC is optimal for submodular clique potential it is significantly better than the compared methods even for problems with non-submodular clique potential.
Scanline Sampler without Detailed Balance: An Efficient MCMC for MRF Optimization
Authors: Wonsik Kim, Kyoung Mu Lee
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6

Markov chain Monte Carlo (MCMC) is an elegant tool, widely used in variety of areas. In computer vision, it has been used for the inference on the Markov random field model (MRF). However, MCMC less concerned than other deterministic approaches although it converges to global optimal solution in theory. The major obstacle is its slow convergence. To come up with faster sampling method, we investigate two ideas: breaking detailed balance and updating multiple nodes at a time. Although detailed balance is considered to be essential element of MCMC, it actually is not the necessary condition for the convergence. In addition, exploiting the structure of MRF, we introduce a new kernel which updates multiple nodes in a scanline rather than a single node. Those two ideas are integrated in a novel way to develop an efficient method called scanline sampler without detailed balance. In experimental section, we apply our method to the OpenGM2 benchmark of MRF optimization and show the proposed method achieves faster convergence than the conventional approaches.
Higher-Order Clique Reduction Without Auxiliary Variables
Authors: Hiroshi Ishikawa
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6

We introduce a method to reduce most higher-order terms of Markov Random Fields with binary labels into lower-order ones without introducing any new variables, while keeping the minimizer of the energy unchanged. While the method does not reduce all terms, it can be used with existing techniques that transformsarbitrary terms (by introducing auxiliary variables) and improve the speed. The method eliminates a higher-order term in the polynomial representation of the energy by finding the value assignment to the variables involved that cannot be part of a global minimizer and increasing the potential value only when that particular combination occurs by the exact amount that makes the potential of lower order. We also introduce a faster approximation that forego the guarantee of exact equivalence of minimizer in favor of speed. With experiments on the same field of experts dataset used in previous work, we show that the roof-dual algorithm after the reduction labels significantly more variables and the energy converges more rapidly.