nils-herrmann/clean_scirepeval huggingface dataset:
Large-Scale Weakly Supervised Object Localization via Latent Category Learning

Localizing objects in cluttered backgrounds is challenging under large-scale weakly supervised conditions. Due to the cluttered image condition, objects usually have large ambiguity with backgrounds. Besides, there is also a lack of effective algorithm for large-scale weakly supervised localization in cluttered backgrounds. However, backgrounds contain useful latent information, e.g., the sky in the aeroplane class. If this latent information can be learned, object-background ambiguity can be largely reduced and background can be suppressed effectively. In this paper, we propose the latent category learning (LCL) in large-scale cluttered conditions. LCL is an unsupervised learning method which requires only image-level class labels. First, we use the latent semantic analysis with semantic object representation to learn the latent categories, which represent objects, object parts or backgrounds. Second, to determine which category contains the target object, we propose a category selection strategy by evaluating each category's discrimination. Finally, we propose the online LCL for use in large-scale conditions. Evaluation on the challenging PASCAL Visual Object Class (VOC) 2007 and the large-scale imagenet large-scale visual recognition challenge 2013 detection data sets shows that the method can improve the annotation precision by 10% over previous methods. More importantly, we achieve the detection precision which outperforms previous results by a large margin and can be competitive to the supervised deformable part model 5.0 baseline on both data sets.
Principal axis-based correspondence between multiple cameras for people tracking

Visual surveillance using multiple cameras has attracted increasing interest in recent years. Correspondence between multiple cameras is one of the most important and basic problems which visual surveillance using multiple cameras brings. In this paper, we propose a simple and robust method, based on principal axes of people, to match people across multiple cameras. The correspondence likelihood reflecting the similarity of pairs of principal axes of people is constructed according to the relationship between "ground-points" of people detected in each camera view and the intersections of principal axes detected in different camera views and transformed to the same view. Our method has the following desirable properties; 1) camera calibration is not needed; 2) accurate motion detection and segmentation are less critical due to the robustness of the principal axis-based feature to noise; 3) based on the fused data derived from correspondence results, positions of people in each camera view can be accurately located even when the people are partially occluded in all views. The experimental results on several real video sequences from outdoor environments have demonstrated the effectiveness, efficiency, and robustness of our method.
Human Pose Estimation and Tracking via Parsing a Tree Structure Based Human Model

Human pose estimation and tracking is the task of determining the states (location, orientation, and scale) of each body part over time. It is important for many vision understanding applications, such as visual interactive gaming, immersive virtual reality, visual surveillance, and content-based image retrieval. However, it remains a challenging task due to unknown image background, presence of clutter and especially the high dimensional state space (usually 30+ dimensions). In this paper, we contribute to human pose estimation and tracking in two aspects. First, we design two efficient Markov Chain dynamics under the data-driven Markov Chain Monte Carlo framework to effectively explore the high dimensional state space. Second, we parse the tree structure state space into a lexicographic order according to the image observations and body topology, and the optimization process is conducted in this order. This realizes a much more efficient exploration of the state space than the sampling based search or exhaustive search, and thus achieves a tremendous speed-up. Experimental results demonstrate the efficiency and effectiveness of the proposed method in estimating and tracking various kinds of human poses, even against cluttered backgrounds, in poor illumination or under partial self-occlusion.
Efficient human pose estimation via parsing a tree structure based human model

Human pose estimation is the task of determining the states (location, orientation and scale) of each body part. It is important for many vision understanding applications, e.g. visual interactive gaming, immersive virtual reality, content-based image retrieval, etc. However, it remains a challenging task because of unknown image background, presence of clutter, partial occlusion and especially the high dimensional state space (usually 30+ dimensions). In this paper, we contribute to human pose estimation in two aspects. First, we design two efficient Markov Chain dynamics under the data-driven Markov Chain Monte Carlo (DDMCMC) framework to effectively explore the complex solution space. Second, we parse the tree structure state space into a lexicographic order according to the image observations and body topology, and the optimization process is conducted in this order. This realizes a much more efficient exploration than the sampling based search and exhaustive search, and thus achieves a tremendous speed-up. Experimental results demonstrate the efficiency and effectiveness of the proposed method in estimating various kinds of human poses, even with cluttered background , poor illumination or partial self-occlusion.
Elapsed Time in Human Gait Recognition: A New Approach

Human gait is an effective biometric source for human identification and visual surveillance; therefore human gait recognition becomes to be a hot topic in recent research. However, the elapsed time problem, which is in its infancy, still receives poor performance. In this paper, we introduce a novel discriminant analysis method to improve the performance. The new model inherits the merits from the tensor rank one analysis, which handles the small samples size problem naturally, and the linear discriminant analysis, which is optimal for classification. Although 2 DLDA and DATR also benefit from these two methods, they cannot converge during the training procedure. This means they can be hardly utilized for practical applications. Based on a lot of experiments on elapsed time problem in human gait recognition, the new method is demonstrated to significantly outperform the existing appearance-based methods, such as the principle component analysis, the linear discriminant analysis, and the tensor rank one analysis
AdaBoost-Based Algorithm for Network Intrusion Detection

Network intrusion detection aims at distinguishing the attacks on the Internet from normal use of the Internet. It is an indispensable part of the information security system. Due to the variety of network behaviors and the rapid development of attack fashions, it is necessary to develop fast machine-learning-based intrusion detection algorithms with high detection rates and low false-alarm rates. In this correspondence, we propose an intrusion detection algorithm based on the AdaBoost algorithm. In the algorithm, decision stumps are used as weak classifiers. The decision rules are provided for both categorical and continuous features. By combining the weak classifiers for continuous features and the weak classifiers for categorical features into a strong classifier, the relations between these two different types of features are handled naturally, without any forced conversions between continuous and categorical features. Adaptable initial weights and a simple strategy for avoiding overfitting are adopted to improve the performance of the algorithm. Experimental results show that our algorithm has low computational complexity and error rates, as compared with algorithms of higher computational complexity, as tested on the benchmark sample data.
Sequential particle swarm optimization for visual tracking

Visual tracking usually involves an optimization process for estimating the motion of an object from measured images in a video sequence. In this paper, a new evolutionary approach, PSO (particle swarm optimization), is adopted for visual tracking. Since the tracking process is a dynamic optimization problem which is simultaneously influenced by the object state and the time, we propose a sequential particle swarm optimization framework by incorporating the temporal continuity information into the traditional PSO algorithm. In addition, the parameters in PSO are changed adaptively according to the fitness values of particles and the predicted motion of the tracked object, leading to a favourable performance in tracking applications. Furthermore, we show theoretically that, in a Bayesian inference view, the sequential PSO framework is in essence a multilayer importance sampling based particle filter. Experimental results demonstrate that, compared with the state-of-the-art particle filter and its variation - the unscented particle filter, the proposed tracking algorithm is more robust and effective, especially when the object has an arbitrary motion or undergoes large appearance changes.
Guest Editorial Introduction to the Special Issue on Large-Scale Video Analytics for Enhanced Security: Algorithms and Systems

Due to the rapid increase of the number of cameras used in the video surveillance and the huge needs of the smart city and public security, video surveillance by human beings is no longer suitable. Hence, since the end of the last century, video analytics for security or visual surveillance has become one of the hottest research topics. Wide-area video surveillance systems can have extremely high data rates and high data volumes. Therefore, the challenge of video analytics is to extract meaningful information efficiently from the huge flow of video data in order to produce high-level semantic descriptions of the activities occurring in the area under surveillance.
Traffic accident prediction using 3-D model-based vehicle tracking

Intelligent visual surveillance for road vehicles is the key to developing autonomous intelligent traffic systems. Recently, traffic incident detection employing computer vision and image processing has attracted much attention. In this paper, a probabilistic model for predicting traffic accidents using three-dimensional (3-D) model-based vehicle tracking is proposed. Sample data including motion trajectories are first obtained by 3-D model-based vehicle tracking. A fuzzy self-organizing neural network algorithm is then applied to learn activity patterns from the sample trajectories. Finally, vehicle activity is predicted by locating and matching each partial trajectory with the learned activity patterns, and the occurrence probability of a traffic accident is determined. Experiments show the effectiveness of the proposed algorithms.
Gender recognition based on local body motions

Human body motions, including gait information, are a promising biometrics resource. In this paper, the human silhouette is segmented into seven components for visual surveillance applications, namely, head, arm, body, thigh, front-leg, back-leg, and feet. The legs are classified as front-leg or back-leg because of the bipedal walking style: during walking, the left-leg and the right-leg are in front or at the back in turn. The motions of the individual components and of a number of combinations of components are then studied for gender recognition. For HumanID recognition under different cases, the performances of and underlying links amongst the seven human gait components are analyzed.
Multi-object tracking via species based particle swarm optimization

Multiple object tracking is particularly challenging when many objects with similar appearances occlude one another. Most existing approaches concatenate the states of different objects, view the multi-object tracking as a joint motion estimation problem and search for the best state of the joint motion in a rather high dimensional space. However, this centralized framework suffers a great computational load. We brings a new view to the tracking problem from a swarm intelligence perspective. In analogy with the foraging behavior of the bird flocks, we propose a species based PSO (particle swarm optimization) algorithm for multiple object tracking, in which the global swarm is divided into many species according to the number of objects, and each species searches for its object and maintains it. The interaction between different objects is modeled as species competition and repulsion, and the occlusion relationship is implicitly deduced from the ‘power’ of each species, which is effectively evaluated by the image observations. Therefore, our approach decentralizes the joint tracker to a set of individual trackers, each of which tries to maximize its visual evidence. Experimental results demonstrate the efficiency and effectiveness of our method.
Multi-View Multi-Instance Learning Based on Joint Sparse Representation and Multi-View Dictionary Learning

In multi-instance learning (MIL), the relations among instances in a bag convey important contextual information in many applications. Previous studies on MIL either ignore such relations or simply model them with a fixed graph structure so that the overall performance inevitably degrades in complex environments. To address this problem, this paper proposes a novel multi-view multi-instance learning algorithm (M<inline-formula><tex-math notation="LaTeX">$^2$ </tex-math><alternatives><inline-graphic xlink:href="li-ieq1-2669303.gif"/></alternatives></inline-formula>IL) that combines multiple context structures in a bag into a unified framework. The novel aspects are: (i) we propose a sparse <inline-formula><tex-math notation="LaTeX">$\varepsilon$</tex-math><alternatives> <inline-graphic xlink:href="li-ieq2-2669303.gif"/></alternatives></inline-formula>-graph model that can generate different graphs with different parameters to represent various context relations in a bag, (ii) we propose a multi-view joint sparse representation that integrates these graphs into a unified framework for bag classification, and (iii) we propose a multi-view dictionary learning algorithm to obtain a multi-view graph dictionary that considers cues from all views simultaneously to improve the discrimination of the M<inline-formula><tex-math notation="LaTeX"> $^2$</tex-math><alternatives><inline-graphic xlink:href="li-ieq3-2669303.gif"/></alternatives></inline-formula>IL. Experiments and analyses in many practical applications prove the effectiveness of the M<inline-formula> <tex-math notation="LaTeX">$^2$</tex-math><alternatives><inline-graphic xlink:href="li-ieq4-2669303.gif"/> </alternatives></inline-formula>IL.
Robust Head Tracking Based on Multiple Cues Fusion in the Kernel-Bayesian Framework

This paper presents a robust head tracking algorithm based on multiple cues fusion in a kernel-Bayesian framework. In this algorithm, the object to be tracked is characterized using a spatial-constraint mixture of the Gaussians-based appearance model and a multichannel chamfer matching-based shape model. These two models complement each other and their combination is discriminative in distinguishing the object from the background. A selective updating technique for the appearance model is employed to accommodate appearance and illumination changes. Meantime, the kernel method-mean shift algorithm is embedded into the Bayesian framework to give a heuristic prediction in the hypotheses generation process. This alleviates the great computational load suffered by conventional Bayesian trackers. Experimental results demonstrate that the proposed algorithm is effective.
Multiple Object Tracking Via Species-Based Particle Swarm Optimization

Multiple object tracking is particularly challenging when many objects with similar appearances occlude one another. Most existing approaches concatenate the states of different objects, view the multi-object tracking as a joint motion estimation problem and search for the best state of the joint motion in a rather high dimensional space. However, this centralized framework suffers from a high computational load. We bring a new view to the tracking problem from a swarm intelligence perspective. In analogy with the foraging behavior of bird flocks, we propose a species-based particle swarm optimization algorithm for multiple object tracking, in which the global swarm is divided into many species according to the number of objects, and each species searches for its object and maintains track of it. The interaction between different objects is modeled as species competition and repulsion, and the occlusion relationship is implicitly deduced from the “power” of each species, which is a function of the image observations. Therefore, our approach decentralizes the joint tracker to a set of individual trackers, each of which tries to maximize its visual evidence. Experimental results demonstrate the efficiency and effectiveness of our method.
Atomic signal models based on recursive filter banks

Time-frequency atomic models are useful for signal analysis, modification, and coding, especially when the time-frequency behavior of the atoms matches the behavior of the signal. Such adaptive representations can be derived using the matching pursuit algorithm with an overcomplete dictionary of time-frequency atoms. In this paper, we consider matching pursuit with atoms constructed by coupling causal and anticausal damped sinusoids. These provide advantages over symmetric Gabor atoms for modeling signals with transient behavior, such as music. Furthermore, the matching pursuit computation is simplified by the structure of the atoms; expansions based on these atoms can be derived using simple recursive filter banks.
Set theoretic compression with an application to image coding

We discuss the concept of set theoretic compression where the input signal is implicitly encoded by the specification of a set which contains it, rather than an estimate which approximates it. This approach assumes the reconstruction of an estimate from the encoded set information only at the decoder side. We explain the motivations of this approach for high signal compression and encoding simplification, and the implication of more complex decoding. We then present the tools to support the approach. We finally show a demonstration of this approach in a particular application of image coding.<<ETX>>
On the bandlimitedness of the plenoptic function

Image based-rendering (IBR) can be seen as the sampling and reconstruction of the plenoptic function. The question of the minimum sampling rate in IBR can be addressed via spectral analysis of the plenoptic function. We study a model of the scene where bandlimited images are "painted" on surfaces (e.g. of objects or walls). We show that, in general, the plenoptic function is not bandlimited unless the surfaces are flat. We then characterize the spectral decay of the plenoptic function for this model.
Sampling and Exact Reconstruction of Pulses with Variable Width

Recent sampling results enable the reconstruction of signals composed of streams of fixed-shaped pulses. These results have found applications in topics as varied as channel estimation, biomedical imaging and radio astronomy. However, in many real signals, the pulse shapes vary throughout the signal. In this paper, we show how to sample and perfectly reconstruct Lorentzian pulses with variable width. In the noiseless case, perfect recovery is guaranteed by a set of theorems. In addition, we verify that our algorithm is robust to model mismatch and noise. This allows us to apply the technique to two practical applications: electrocardiogram (ECG) compression and bidirectional reflectance distribution function (BRDF) sampling. ECG signals are one dimensional, but the BRDF is a higher dimensional signal, which is more naturally expressed in a spherical coordinate system; this motivated us to extend the theory to the 2D and spherical cases. Experiments on real data demonstrate the viability of the proposed model for ECG acquisition and compression, as well as the efficient representation and low-rate sampling of specular BRDFs.
Where You Are Is Who You Are: User Identification by Matching Statistics

Most users of online services have unique behavioral or usage patterns. These behavioral patterns can be exploited to identify and track users by using only the observed patterns in the behavior. We study the task of identifying users from statistics of their behavioral patterns. In particular, we focus on the setting in which we are given histograms of users' data collected during two different experiments. We assume that, in the first data set, the users' identities are anonymized or hidden and that, in the second data set, their identities are known. We study the task of identifying the users by matching the histograms of their data in the first data set with the histograms from the second data set. In recent works, the optimal algorithm for this user identification task is introduced. In this paper, we evaluate the effectiveness of this method on three different types of data sets with up to 50 000 users, and in multiple scenarios. Using data sets such as call data records, web browsing histories, and GPS trajectories, we demonstrate that a large fraction of users can be easily identified given only histograms of their data; hence, these histograms can act as users' fingerprints. We also verify that simultaneous identification of users achieves better performance compared with one-by-one user identification. Furthermore, we show that using the optimal method for identification indeed gives higher identification accuracy than the heuristics-based approaches in the practical scenarios. The accuracy obtained under this optimal method can thus be used to quantify the maximum level of user identification that is possible in such settings. We show that the key factors affecting the accuracy of the optimal identification algorithm are the duration of the data collection, the number of users in the anonymized data set, and the resolution of the data set. We also analyze the effectiveness of k-anonymization in resisting user identification attacks on these data sets.
Optimal sensor architecture selection for health management of complex systems

With technological progress, humans tend to create engineering systems with constantly increasing complexity and higher operational requirements. Many complex systems require the use of a Health Management (HM) solution to ensure safety and enable lifecycle properties management of the system. HM solutions such as Integrated Vehicle Health Management (IVHM) hinge mainly on data obtained from sensors. Sensors or collections of sensors, forming sensor architectures constitute an important fraction of the cost of an HM solution and thus have to be carefully designed. However, the trade-off between cost and performance of a sensor architecture is not yet well understood. In the light of this, we have developed a Pareto optimal sensor architecture selection tool for dynamic systems, which integrates performance and cost and aims at aiding design decisions. The tool uses a performance metric based on Mean Square Error (MSE) and derived from the observability matrix of an estimated state space model for a nominal system operation as well as for different system failure modes. The tool is applied to a case study involving a ducted fan, which is a dynamic system and a common mechanical set-up used for propulsion applications. This system can exhibit different mechanical as well as electrical failure modes throughout its lifecycle, which can be managed using a sensor architecture. We consider 63 possible sensor architectures (all the possible combinations out of six sensors) and the tool reduces the choice to only 13 Pareto optimal ones.