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18
Hough Forests for Object Detection, Tracking, and Action Recognition
"... The paper introduces Hough forests which are random forests adapted to perform a generalized Hough transform in an efficient way. Compared to previous Hough-based systems such as implicit shape models, Hough forests improve the performance of the generalized Hough transform for object detection on a ..."
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Cited by 97 (23 self)
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The paper introduces Hough forests which are random forests adapted to perform a generalized Hough transform in an efficient way. Compared to previous Hough-based systems such as implicit shape models, Hough forests improve the performance of the generalized Hough transform for object detection on a categorical level. At the same time, their flexibility permits extensions of the Hough transform to new domains such as object tracking and action recognition. Hough forests can be regarded as task-adapted codebooks of local appearance that allow fast supervised training and fast matching at test time. They achieve high detection accuracy since the entries of such codebooks are optimized to cast Hough votes with small variance, and since their efficiency permits dense sampling of local image patches or video cuboids during detection. The efficacy of Hough forests for a set of computer vision tasks is validated through experiments on a large set of publicly available benchmark datasets and comparisons with the state-of-the-art.
Hough-based Tracking of Non-rigid Objects
- IN: ICCV
, 2011
"... Online learning has shown to be successful in tracking of previously unknown objects. However, most approaches are limited to a bounding-box representation with fixed aspect ratio. Thus, they provide a less accurate foreground/background separation and cannot handle highly non-rigid and articulated ..."
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Cited by 46 (5 self)
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Online learning has shown to be successful in tracking of previously unknown objects. However, most approaches are limited to a bounding-box representation with fixed aspect ratio. Thus, they provide a less accurate foreground/background separation and cannot handle highly non-rigid and articulated objects. This, in turn, increases the amount of noise introduced during online self-training. In this paper, we present a novel tracking-by-detection approach to overcome this limitation based on the generalized Hough-transform. We extend the idea of Hough Forests to the online domain and couple the votingbased detection and back-projection with a rough segmentation based on GrabCut. This significantly reduces the amount of noisy training samples during online learning and thus effectively prevents the tracker from drifting. In the experiments, we demonstrate that our method successfully tracks a variety of previously unknown objects even under heavy non-rigid transformations, partial occlusions, scale changes and rotations. Moreover, we compare our tracker to state-of-the-art methods (both bounding-boxbased as well as part-based) and show robust and accurate tracking results on various challenging sequences.
A Survey of Appearance Models in Visual Object Tracking
"... Visual object tracking is a significant computer vision task which can be applied to many domains such as visual surveillance, human computer interaction, and video compression. Despite extensive research on this topic, it still suffers from difficulties in handling complex object appearance changes ..."
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Cited by 16 (1 self)
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Visual object tracking is a significant computer vision task which can be applied to many domains such as visual surveillance, human computer interaction, and video compression. Despite extensive research on this topic, it still suffers from difficulties in handling complex object appearance changes caused by factors such as illumination variation, partial occlusion, shape deformation, and camera motion. Therefore, effective modeling of the 2D appearance of tracked objects is a key issue for the success of a visual tracker. In the literature, researchers have proposed a variety of 2D appearance models. To help readers swiftly learn the recent advances in 2D appearance models for visual object tracking, we contribute this survey, which provides a detailed review of the existing 2D appearance models. In particular, this survey takes a module-based architecture that enables readers to easily grasp the key points of visual object tracking. In this survey, we first decompose the problem of appearance modeling into two different processing stages: visual representation and statistical modeling. Then, different 2D appearance models are categorized and discussed with respect to their composition modules. Finally, we address several issues of interest as well as the remaining challenges for future research on this topic. The contributions of this survey are four-fold. First, we review the literature of visual representations according to their feature-construction mechanisms (i.e., local and global). Second, the existing statistical modeling schemes for tracking-bydetection are reviewed according to their model-construction mechanisms: generative, discriminative, and hybrid generativediscriminative. Third, each type of visual representations or statistical modeling techniques is analyzed and discussed from
H.: Improving classifiers with unlabeled weakly-related videos
, 2011
"... Current state-of-the-art object classification systems are trained using large amounts of hand-labeled images. In this paper, we present an approach that shows how to use unlabeled video sequences, comprising weakly-related object categories towards the target class, to learn better classifiers for ..."
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Cited by 8 (1 self)
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Current state-of-the-art object classification systems are trained using large amounts of hand-labeled images. In this paper, we present an approach that shows how to use unlabeled video sequences, comprising weakly-related object categories towards the target class, to learn better classifiers for tracking and detection. The underlying idea is to exploit the space-time consistency of moving objects to learn classifiers that are robust to local transformations. In particular, we use dense optical flow to find moving objects in videos in order to train part-based random forests that are insensitive to natural transformations. Our method, which is called Video Forests, can be used in two settings: first, labeled training data can be regularized to force the trained classifier to generalize better towards small local transformations. Second, as part of a tracking-by-detection approach, it can be used to train a general codebook solely on pair-wise data that can then be applied to tracking of instances of a priori unknown object categories. In the experimental part, we show on benchmark datasets for both tracking and detection that incorporating unlabeled videos into the learning of visual classifiers leads to improved results. 1.
Gool, L.: On-line hough forests
- In: British Machine Vision Conf. (2011
"... Hough forests have emerged as a powerful and versatile method, which achieves state-of-the-art results on various computer vision applications, ranging from object detection over pose estimation to action recognition. The original method operates in offline mode, assuming to have access to the entir ..."
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Cited by 4 (1 self)
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Hough forests have emerged as a powerful and versatile method, which achieves state-of-the-art results on various computer vision applications, ranging from object detection over pose estimation to action recognition. The original method operates in offline mode, assuming to have access to the entire training set at once. This limits its applicability in domains where data arrives sequentially or when large amounts of data have to be exploited. In these cases, on-line approaches naturally would be beneficial. To this end, we propose an on-line extension of Hough forests, which is based on the principle of letting the trees evolve on-line while the data arrives sequentially, for both classification and regression. We further propose a modified version of off-line Hough forests, which only needs a small subset of the training data for optimization. In the experiments, we show that using these formulations, the classification results of classic Hough forests could be reached or even outperformed, while being orders of magnitudes faster. Furthermore, our method allows for tracking arbitrary objects without requiring any prior knowledge. We present state-of-the-art tracking results on publicly available data sets. 1
State of the art report on video-based graphics and video visualization,”
- Comp. Graph. Forum,
, 2012
"... Abstract In recent years, a collection of new techniques which deal with video as input data ..."
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Cited by 4 (1 self)
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Abstract In recent years, a collection of new techniques which deal with video as input data
DETECTING
"... Abstract—The paper introduces Hough forests, which are random forests adapted to perform a generalized Hough transform in an efficient way. Compared to previous Hough-based systems such as implicit shape models, Hough forests improve the performance of the generalized Hough transform for object dete ..."
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Cited by 3 (0 self)
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Abstract—The paper introduces Hough forests, which are random forests adapted to perform a generalized Hough transform in an efficient way. Compared to previous Hough-based systems such as implicit shape models, Hough forests improve the performance of the generalized Hough transform for object detection on a categorical level. At the same time, their flexibility permits extensions of the Hough transform to new domains such as object tracking and action recognition. Hough forests can be regarded as task-adapted codebooks of local appearance that allow fast supervised training and fast matching at test time. They achieve high detection accuracy since the entries of such codebooks are optimized to cast Hough votes with small variance and since their efficiency permits dense sampling of local image patches or video cuboids during detection. The efficacy of Hough forests for a set of computer vision tasks is validated through experiments on a large set of publicly available benchmark data sets and comparisons with the state-of-the-art. Index Terms—Hough transform, object detection, tracking, action recognition. Ç
Object Tracking by Oversampling Local Features
, 2014
"... In this paper, we present the ALIEN tracking method that exploits oversampling of local invariant representations to build a robust object/context discriminative classifier. To this end, we use multiple instances of scale invariant local features weakly aligned along the object template. This allows ..."
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Cited by 3 (0 self)
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In this paper, we present the ALIEN tracking method that exploits oversampling of local invariant representations to build a robust object/context discriminative classifier. To this end, we use multiple instances of scale invariant local features weakly aligned along the object template. This allows taking into account the 3D shape deviations from planarity and their interactions with shadows, occlusions and sensor quantization for which no invariant representations can be defined. A non parametric learning algorithm based on the transitive matching property discriminates the object from the context and prevents improper object template updating during occlusion. We show that our learning rule has asymptotic stability under mild conditions and confirms the drift-free capability of the method in long term tracking. A real-time implementation of the ALIEN tracker has been evaluated in comparison with the state of the art tracking systems on an extensive set of publicly available video sequences that represent most of the critical conditions occurring in real tracking environments. We have reported superior or equal performance in most of the cases and verified tracking with no drift in very long video sequences.
Ordinal Random Forests for Object Detection
"... Abstract. In this paper, we present a novel formulation of Random Forests, which introduces order statistics into the splitting functions of nodes. Order statistics, in general, neglect the absolute values of single feature dimensions and just consider the ordering of different feature dimensions. R ..."
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Cited by 2 (0 self)
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Abstract. In this paper, we present a novel formulation of Random Forests, which introduces order statistics into the splitting functions of nodes. Order statistics, in general, neglect the absolute values of single feature dimensions and just consider the ordering of different feature dimensions. Recent works showed that such statistics have more discriminative power than just observing single feature dimensions. However, they were just used as a preprocessing step, transforming data into a higher dimensional feature space, or were limited to just consider two feature dimensions. In contrast, we integrate order statistics into the Random Forest framework, and thus avoid explicit mapping onto higher dimensional spaces. In this way, we can also exploit more than two feature dimensions, resulting in increased discriminative power. Moreover, we show that this idea can easily be extended for the popular Hough Forest framework. The experimental results demonstrate that using splitting functions building on order statistics can improve both, the performance for classification tasks (using Random Forests) and for object detection (using Hough Forests). 1
