nils-herrmann/clean_scirepeval huggingface dataset:
Stripe mesh based disparity estimation by using 3-D Hough transform

This correspondence presents a novel matching technique for disparity estimation. The technique combines advantages of an adaptive stripe-based mesh structure and Hough transform. First, a mesh that is composed of triangular patches and fits to the depth changes is generated by using edge maps. In each patch, the depth of the scene is approximated by a surface. A search space is built by difference maps that are obtained by subtracting the left image from the shifted versions of the right image. From the search space, 3-D Hough spaces are produced such that a point of the Hough space represents a surface in the search space. Then, a matching algorithm finds the combination of surfaces that gives minimum matching error in a neighborhood around patches by using Hough spaces. Continuity and smoothness constraints are formulated as probability density functions that modify the Hough spaces of the following patches by using the estimated surfaces for the previous ones. Our experiments demonstrate the accuracy of the proposed method.
Unconstrained 1D range and 2D image based human detection

An accurate and computationally very fast multimodal human detector is presented. This 1D+2D detector fuses 1D range scan and 2D image information via an effective geometric descriptor and a silhouette based visual representation within a radial basis function kernel support vector machine learning framework. Unlike the existing approaches, the proposed 1D+2D detector does not make any restrictive assumptions on the range scan positions, thus it is applicable to a wide range of real-life detection tasks. To analyze the discriminative power of the geometric descriptor, a range scan only version, 1D+, is also evaluated. Extensive experiments demonstrate that the 1D+2D detector works robustly under challenging imaging conditions and achieves several orders of magnitude performance improvement while reducing the computational load drastically. In addition, a new multi-modal (LIDAR, depth image, optical image) dataset, DontHitMe, is introduced.  It depicts challenging illumination conditions in indoors and outdoors environments and is publicly available to our community.
Human Detection via Classification on Riemannian Manifolds

We present a new algorithm to detect humans in still images utilizing covariance matrices as object descriptors. Since these descriptors do not lie on a vector space, well known machine learning techniques are not adequate to learn the classifiers. The space of d-dimensional nonsingular covariance matrices can be represented as a connected Riemannian manifold. We present a novel approach for classifying points lying on a Riemannian manifold by incorporating the a priori information about the geometry of the space. The algorithm is tested on INRIA human database where superior detection rates are observed over the previous approaches.
Detection and characterization of Intrinsic symmetry of 3D shapes

A comprehensive framework for detection and characterization of partial intrinsic symmetry over 3D shapes is proposed. To identify prominent symmetric regions which overlap in space and vary in form, the proposed framework is decoupled into a Correspondence Space Voting (CSV) procedure followed by a Transformation Space Mapping (TSM) procedure. In the CSV procedure, significant symmetries are first detected by identifying surface point pairs on the input shape that exhibit local similarity in terms of their intrinsic geometry while simultaneously maintaining an intrinsic distance structure at a global level. To allow detection of potentially overlapping symmetric shape regions, a global intrinsic distance-based voting scheme is employed to ensure the inclusion of only those point pairs that exhibit significant intrinsic symmetry. In the TSM procedure, the Functional Map framework is employed to generate the final map of symmetries between point pairs. The TSM procedure ensures the retrieval of the underlying dense correspondence map throughout the 3D shape that follows a particular symmetry. The TSM procedure is also shown to result in the formulation of a metric symmetry space where each point in the space represents a specific symmetry transformation and the distance between points represents the complexity between the corresponding transformations. Experimental results show that the proposed framework can successfully analyze complex 3D shapes that possess rich symmetries.
Object detection via boosted deformable features

It is a common practice to model an object for detection tasks as a boosted ensemble of many models built on features of the object. In this context, features are defined as subregions with fixed relative locations and extents with respect to the object's image window. We introduce using deformable features with boosted ensembles. A deformable features adapts its location depending on the visual evidence in order to match the corresponding physical feature. Therefore, deformable features can better handle deformable objects. We empirically show that boosted ensembles of deformable features perform significantly better than boosted ensembles of fixed features for human detection.
Learning object trajectory patterns by spectral clustering

We develop a trajectory pattern learning method that has two significant advantages over past work. First, we represent trajectories in the HMM parameter space, thus we overcome the normalization problems of existing methods. Second, we determine common trajectory paths by analyzing the optimal cluster number rather than using a predefined number of clusters. We compute affinity matrices and apply eigenvector decomposition to find clusters. We prove that the number of clusters governs the number of eigenvectors used to span the feature affinity space. We are thus able to determine automatically the optimal number of patterns. We show that the proposed algorithm accurately detects common paths for various camera setups
Regressed Importance Sampling on Manifolds for Efficient Object Tracking

In this paper, a new integrated particle filter is proposed for video object tracking. After particles are generated by importance sampling, each particle is regressed on the transformation space where the mapping function is learned offline by regression on pose manifold using Lie algebra, leading to a more effective allocation of particles. Experimental results on synthetic and real sequences clearly demonstrate the improved pose (affine) tracking performance of the proposed method compared with the original regression tracker and particle filters.
Compressed Domain Video Object Segmentation

We present a compressed domain video object segmentation method for the MPEG encoded video sequences. For a fraction of the raw domain analysis, compressed domain segmentation provides the essential a priori information to many vision tasks from surveillance to transcoding that require fast processing of large volumes of data where pixel-resolution boundary extraction is not required. Our method generates accurate segmentation maps in block resolution at hierarchically varying object levels, which empowers application to determine the most pertinent partition of images. It exploits the block structure of the compressed video to minimize the amount of data to be processed. All the available motion flow within a group of pictures is projected onto a single layer, which also consists of the frequency decomposition of color pattern. Then, by starting from the blocks where the spatial energy is small, it expands homogeneous regions while automatically adapting local similarity criteria. We also formulate an alternative solution that applies a kernel-based clustering where separate spatial, transform, and motion kernels are used to establish the affinity. We show that both region expansion and mean shift produce similar results as the computationally expensive raw domain segmentation. Finally, a binary clustering iteratively merges the most similar regions to generate a hierarchical partition tree.
Compressive Clustering of High-Dimensional Data

In this paper we focus on realistic clustering problems where the input data is high-dimensional and the clusters have complex, multimodal distribution. In this challenging setting the conventional methods, such as k-centers family, hierarchical clustering or those based on model fitting, are inefficient and typically converge far from the globally optimal solution. As an alternative, we propose a novel unsupervised learning approach which is based on the compressive sensing paradigm. The key idea underlying our algorithm is to monitor the distance between the test sample and its principal projection in each cluster, and continue re-assigning it to the cluster yielding the smallest residual. As a result, we obtain an iterative procedure which, under the compressive assumptions, minimizes the total reconstruction error of all samples from their nearest clusters. To evaluate the proposed approach, we have conducted a series of experiments involving various image collections where the task was to automatically group similar objects. Comparison of the obtained results with those yielded by the state-of-the-art clustering methods provides evidence for high discriminative power of our algorithm.
DeepTrack: Learning Discriminative Feature Representations Online for Robust Visual Tracking.

Deep neural networks, albeit their great success on feature learning in various computer vision tasks, are usually considered as impractical for online visual tracking, because they require very long training time and a large number of training samples. In this paper, we present an efficient and very robust tracking algorithm using a single convolutional neural network (CNN) for learning effective feature representations of the target object in a purely online manner. Our contributions are multifold. First, we introduce a novel truncated structural loss function that maintains as many training samples as possible and reduces the risk of tracking error accumulation. Second, we enhance the ordinary stochastic gradient descent approach in CNN training with a robust sample selection mechanism. The sampling mechanism randomly generates positive and negative samples from different temporal distributions, which are generated by taking the temporal relations and label noise into account. Finally, a lazy yet effective updating scheme is designed for CNN training. Equipped with this novel updating algorithm, the CNN model is robust to some long-existing difficulties in visual tracking, such as occlusion or incorrect detections, without loss of the effective adaption for significant appearance changes. In the experiment, our CNN tracker outperforms all compared state-of-the-art methods on two recently proposed benchmarks, which in total involve over 60 video sequences. The remarkable performance improvement over the existing trackers illustrates the superiority of the feature representations, which are learned purely online via the proposed deep learning framework.
Multi-scale salient object detection with pyramid spatial pooling

Salient object detection is a challenging task in complex compositions depicting multiple objects of different scales. Albeit the recent progress thanks to the convolutional neural networks, the state-of-the-art salient object detection methods still fall short to handle such real-life scenarios. In this paper, we propose a new method called MP-SOD that exploits both Multi-Scale feature fusion and Pyramid spatial pooling to detect salient object regions in varying sizes. Our framework consists of a front-end network and two multi-scale fusion modules. The front-end network learns an end-to-end mapping from the input image to a saliency map, where a pyramid spatial pooling is incorporated to aggregate rich context information from different spatial receptive fields. The multi-scale fusion module integrates saliency cues across different layers, that is from low-level detail patterns to high-level semantic information by concatenating feature maps, to segment out salient objects with multiple scales. Extensive experimental results on eight benchmark datasets demonstrate the superior performance of our method compared with existing methods.
Changedetection.net: A new change detection benchmark dataset

Change detection is one of the most commonly encountered low-level tasks in computer vision and video processing. A plethora of algorithms have been developed to date, yet no widely accepted, realistic, large-scale video dataset exists for benchmarking different methods. Presented here is a unique change detection benchmark dataset consisting of nearly 90,000 frames in 31 video sequences representing 6 categories selected to cover a wide range of challenges in 2 modalities (color and thermal IR). A distinguishing characteristic of this dataset is that each frame is meticulously annotated for ground-truth foreground, background, and shadow area boundaries - an effort that goes much beyond a simple binary label denoting the presence of change. This enables objective and precise quantitative comparison and ranking of change detection algorithms. This paper presents and discusses various aspects of the new dataset, quantitative performance metrics used, and comparative results for over a dozen previous and new change detection algorithms. The dataset, evaluation tools, and algorithm rankings are available to the public on a website1 and will be updated with feedback from academia and industry in the future.
Semi-Supervised Video Object Segmentation with Super-Trajectories

We introduce a semi-supervised video segmentation approach based on an efficient video representation, called as “super-trajectory”. A super-trajectory corresponds to a group of compact point trajectories that exhibit consistent motion patterns, similar appearances, and close spatiotemporal relationships. We generate the compact trajectories using a probabilistic model, which enables handling of occlusions and drifts effectively. To reliably group point trajectories, we adopt the density peaks based clustering algorithm that allows capturing rich spatiotemporal relations among trajectories in the clustering process. We incorporate two intuitive mechanisms for segmentation, called as reverse-tracking and object re-occurrence, for robustness and boosting the performance. Building on the proposed video representation, our segmentation method is discriminative enough to accurately propagate the initial annotations in the first frame onto the remaining frames. Our extensive experimental analyses on three challenging benchmarks demonstrate that, our method is capable of extracting the target objects from complex backgrounds, and even reidentifying them after prolonged occlusions, producing high-quality video object segments. The code and results are available at: https://github.com/wenguanwang/SupertrajectorySeg.
Support Vector Shape: A Classifier-Based Shape Representation

We introduce a novel implicit representation for 2D and 3D shapes based on Support Vector Machine (SVM) theory. Each shape is represented by an analytic decision function obtained by training SVM, with a Radial Basis Function (RBF) kernel so that the interior shape points are given higher values. This empowers support vector shape (SVS) with multifold advantages. First, the representation uses a sparse subset of feature points determined by the support vectors, which significantly improves the discriminative power against noise, fragmentation, and other artifacts that often come with the data. Second, the use of the RBF kernel provides scale, rotation, and translation invariant features, and allows any shape to be represented accurately regardless of its complexity. Finally, the decision function can be used to select reliable feature points. These features are described using gradients computed from highly consistent decision functions instead from conventional edges. Our experiments demonstrate promising results.
Ambiguity detection by fusion and conformity: a spectral clustering approach

Event detection requires interpretation of the "semantically meaningful" object actions. To achieve this task, the gap between the numerical features of objects and the symbolic description of the meaningful activities needs to be bridged. We develop an ambiguity detection framework that has two significant advantages over past work. First, we introduce a fusion method for a set of time-wise and object-wise features including not only the trajectory coordinates but also the histograms and HMM based representations of object's speed, orientation, location, size, and aspect ratio. This fusion method enable detection of events that cannot be detected with the existing trajectory features reported so far. Second, we improve existing spectral clustering algorithms by automatically estimating the optimal number of clusters. Furthermore, we determine the conformity of the objects within the given data space. We compute a separate HMM for each object using a time-series that is composed of the mixture of its features. Then, we construct an aggregated affinity matrix from the pair-wise similarity scores of objects using the HMM's. We apply eigenvector decomposition and obtain object clusters. We show that the number of eigenvectors used in the decomposition is proportional to the optimal number of clusters. We examine the affinity matrix to determine the deviance of objects from common assemblages within the space. Our simulations reveal that the proposed detection methods accurately discover both usual and unusual events.
Learning on lie groups for invariant detection and tracking

This paper presents a novel learning based tracking model combined with object detection. The existing techniques proceed by linearizing the motion, which makes an implicit Euclidean space assumption. Most of the transformations used in computer vision have matrix Lie group structure. We learn the motion model on the Lie algebra and show that the formulation minimizes a first order approximation to the geodesic error. The learning model is extended to train a class specific tracking function, which is then integrated to an existing pose dependent object detector to build a pose invariant object detection algorithm. The proposed model can accurately detect objects in various poses, where the size of the search space is only a fraction compared to the existing object detection methods. The detection rate of the original detector is improved by more than 90% for large transformations.
Fast Detection of Multiple Objects in Traffic Scenes With a Common Detection Framework

Traffic scene perception (TSP) aims to extract accurate real-time on-road environment information, which involves three phases: detection of objects of interest, recognition of detected objects, and tracking of objects in motion. Since recognition and tracking often rely on the results from detection, the ability to detect objects of interest effectively plays a crucial role in TSP. In this paper, we focus on three important classes of objects: traffic signs, cars, and cyclists. We propose to detect all the three important objects in a single learning-based detection framework. The proposed framework consists of a dense feature extractor and detectors of three important classes. Once the dense features have been extracted, these features are shared with all detectors. The advantage of using one common framework is that the detection speed is much faster, since all dense features need only to be evaluated once in the testing phase. In contrast, most previous works have designed specific detectors using different features for each of these three classes. To enhance the feature robustness to noises and image deformations, we introduce spatially pooled features as a part of aggregated channel features. In order to further improve the generalization performance, we propose an object subcategorization method as a means of capturing the intraclass variation of objects. We experimentally demonstrate the effectiveness and efficiency of the proposed framework in three detection applications: traffic sign detection, car detection, and cyclist detection. The proposed framework achieves the competitive performance with state-of-the-art approaches on several benchmark data sets.
Robust Orthonormal Subspace Learning: Efficient Recovery of Corrupted Low-Rank Matrices

Low-rank matrix recovery from a corrupted observation has many applications in computer vision. Conventional methods address this problem by iterating between nuclear norm minimization and sparsity minimization. However, iterative nuclear norm minimization is computationally prohibitive for large-scale data (e.g., video) analysis. In this paper, we propose a Robust Orthogonal Subspace Learning (ROSL) method to achieve efficient low-rank recovery. Our intuition is a novel rank measure on the low-rank matrix that imposes the group sparsity of its coefficients under orthonormal subspace. We present an efficient sparse coding algorithm to minimize this rank measure and recover the low-rank matrix at quadratic complexity of the matrix size. We give theoretical proof to validate that this rank measure is lower bounded by nuclear norm and it has the same global minimum as the latter. To further accelerate ROSL to linear complexity, we also describe a faster version (ROSL+) empowered by random sampling. Our extensive experiments demonstrate that both ROSL and ROSL+ provide superior efficiency against the state-of-the-art methods at the same level of recovery accuracy.
Beyond Local Search: Tracking Objects Everywhere with Instance-Specific Proposals

Most tracking-by-detection methods employ a local search window around the predicted object location in the current frame assuming the previous location is accurate, the trajectory is smooth, and the computational capacity permits a search radius that can accommodate the maximum speed yet small enough to reduce mismatches. These, however, may not be valid always, in particular for fast and irregularly moving objects. Here, we present an object tracker that is not limited to a local search window and has ability to probe efficiently the entire frame. Our method generates a small number of "high-quality" proposals by a novel instance-specific objectness measure and evaluates them against the object model that can be adopted from an existing tracking-by-detection approach as a core tracker. During the tracking process, we update the object model concentrating on hard false-positives supplied by the proposals, which help suppressing distractors caused by difficult background clutters, and learn how to re-rank proposals according to the object model. Since we reduce significantly the number of hypotheses the core tracker evaluates, we can use richer object descriptors and stronger detector. Our method outperforms most recent state-of-the-art trackers on popular tracking benchmarks, and provides improved robustness for fast moving objects as well as for ultra lowframerate videos.
Pushing the Limits of Deep CNNs for Pedestrian Detection

Compared with other applications in computer vision, convolutional neural networks (CNNs) have underperformed on pedestrian detection. A breakthrough was made very recently using sophisticated deep CNN (DCNN) models, with a number of handcrafted features or explicit occlusion handling mechanism. In this paper, we show that by reusing the convolutional feature maps of a DCNN model as image features to train an ensemble of boosted decision models, we are able to achieve the best reported accuracy without using specially designed learning algorithms. We empirically identify and disclose important implementation details. We also show that pixel labeling may be simply combined with a detector to boost the detection performance. By adding complementary handcrafted features such as optical flow, the DCNN-based detector can be further improved. We advance the state-of-the-art results by lowering the log-average miss rate from 11.7% to 8.9% on the Caltech data set and from 11.2% to 8.6% on the Inria data set. We also achieve a comparable result to state-of-the-art approaches on the KITTI data set.