nils-herrmann/clean_scirepeval huggingface dataset:
Robust visual tracking using joint scale-spatial correlation filters

Scale adaptation is crucial to object tracking as the visual size of the target changes continuously. Many existing tracking algorithms, however, simply ignore scale changes either for the consideration of tracking efficiency or the lack of principle ways to scale estimation. In this work, we present an efficient and effective scale adaptive tracking algorithm by proposing a correlation filter based tracker in the joint spatial and scale space. We find that the exhaustive template searching in this joint space can be well modeled by a block-circulant matrix. With the properties of the block-circulant matrices, we prove that the expensive template matching can be transformed to efficient dot product in frequency domain by fast Fourier Transform. Based on these findings, our new tracker significantly improves the robustness and adaptability of previous competitive spatial correlation trackers. On the latest single object tracking benchmark, our tracker advances the state-of-the-art tracking results with a very large margin.
Segment Model Based Vehicle Motion Analysis

Motion analysis is a very attractive research direction in computer vision field. In this paper, we propose a framework for analyzing real vehicle motion in visual traffic surveillance by using Segment Model (SM), which is a kind of probabilistic model. SM can grasp the underlying information of observation sequence by using segment distribution. It has been proved to be more precise than that of HMM. In the experiments, we compare our approach with the template matching method based on the Hausdorff distance and the state space method based on the Hidden Markov Model (HMM). The experimental results show the effectiveness of our approach.
Multi-target Tracking with Motion Context in Tensor Power Iteration

Interactions between moving targets often provide discriminative clues for multiple target tracking (MTT), though many existing approaches ignore such interactions due to difficulty in effectively handling them. In this paper, we model interactions between neighbor targets by pair-wise motion context, and further encode such context into the global association optimization. To solve the resulting global non-convex maximization, we propose an effective and efficient power iteration framework. This solution enjoys two advantages for MTT: First, it allows us to combine the global energy accumulated from individual trajectories and the between-trajectory interaction energy into a united optimization, which can be solved by the proposed power iteration algorithm. Second, the framework is flexible to accommodate various types of pairwise context models and we in fact studied two different context models in this paper. For evaluation, we apply the proposed methods to four public datasets involving different challenging scenarios such as dense aerial borne traffic tracking, dense point set tracking, and semi-crowded pedestrian tracking. In all the experiments, our approaches demonstrate very promising results in comparison with state-of-the-art trackers.
Spatio-Temporal Self-Organizing Map Deep Network for Dynamic Object Detection from Videos

In dynamic object detection, it is challenging to construct an effective model to sufficiently characterize the spatial-temporal properties of the background. This paper proposes a new Spatio-Temporal Self-Organizing Map (STSOM) deep network to detect dynamic objects in complex scenarios. The proposed approach has several contributions: First, a novel STSOM shared by all pixels in a video frame is presented to efficiently model complex background. We exploit the fact that the motions of complex background have the global variation in the space and the local variation in the time, to train STSOM using the whole frames and the sequence of a pixel over time to tackle the variance of complex background. Second, a Bayesian parameter estimation based method is presented to learn thresholds automatically for all pixels to filter out the background. Last, in order to model the complex background more accurately, we extend the single-layer STSOM to the deep network. Then the background is filtered out layer by layer. Experimental results on CDnet 2014 dataset demonstrate that the proposed STSOM deep network outperforms numerous recently proposed methods in the overall performance and in most categories of scenarios.
Human Action Recognition Based on Context-Dependent Graph Kernels

Graphs are a powerful tool to model structured objects, but it is nontrivial to measure the similarity between two graphs. In this paper, we construct a two-graph model to represent human actions by recording the spatial and temporal relationships among local features. We also propose a novel family of context-dependent graph kernels (CGKs) to measure similarity between graphs. First, local features are used as the vertices of the two-graph model and the relationships among local features in the intra-frames and inter-frames are characterized by the edges. Then, the proposed CGKs are applied to measure the similarity between actions represented by the two-graph model. Graphs can be decomposed into numbers of primary walk groups with different walk lengths and our CGKs are based on the context-dependent primary walk group matching. Taking advantage of the context information makes the correctly matched primary walk groups dominate in the CGKs and improves the performance of similarity measurement between graphs. Finally, a generalized multiple kernel learning algorithm with a proposed l12-norm regularization is applied to combine these CGKs optimally together and simultaneously train a set of action classifiers. We conduct a series of experiments on several public action datasets. Our approach achieves a comparable performance to the state-of-the-art approaches, which demonstrates the effectiveness of the two-graph model and the CGKs in recognizing human actions.
Recognition of blue movies by fusion of audio and video

Along with the explosive growth of the Internet, comes the proliferation of pornography. Compared with the pornographic texts and images, blue movies can do much harm to children, due to the greater realism and voyeurism of blue movies. In this paper, a framework for recognizing blue movies by fusing the audio and video information is described. A one-class Gaussian mixture model (GMM) is used to recognize porno-sounds. A generalized contour-based pornographic image recognition algorithm is used to detect pornographic image frames of a video shot. Then a fusion algorithm based on the Bayes theory is employed to combine the recognition results from audio and video. Experimental results demonstrate that our framework which exploits both audio and video modalities is more robust and achieves better performance than one which uses either one alone.
An Incremental DPMM-Based Method for Trajectory Clustering, Modeling, and Retrieval

Trajectory analysis is the basis for many applications, such as indexing of motion events in videos, activity recognition, and surveillance. In this paper, the Dirichlet process mixture model (DPMM) is applied to trajectory clustering, modeling, and retrieval. We propose an incremental version of a DPMM-based clustering algorithm and apply it to cluster trajectories. An appropriate number of trajectory clusters is determined automatically. When trajectories belonging to new clusters arrive, the new clusters can be identified online and added to the model without any retraining using the previous data. A time-sensitive Dirichlet process mixture model (tDPMM) is applied to each trajectory cluster for learning the trajectory pattern which represents the time-series characteristics of the trajectories in the cluster. Then, a parameterized index is constructed for each cluster. A novel likelihood estimation algorithm for the tDPMM is proposed, and a trajectory-based video retrieval model is developed. The tDPMM-based probabilistic matching method and the DPMM-based model growing method are combined to make the retrieval model scalable and adaptable. Experimental comparisons with state-of-the-art algorithms demonstrate the effectiveness of our algorithm.
Web Horror Image Recognition Based on Context-Aware Multi-instance Learning

Along with the ever-growing Web, horror contents sharing in the Internet has interfered with our daily life and affected our, especially children's, health. Therefore horror image recognition is becoming more important for web objectionable content filtering. This paper presents a novel context-aware multi-instance learning (CMIL) model for this task. This work is distinguished by three key contributions. Firstly, the traditional multi-instance learning is extended to context-aware multi-instance learning model through integrating an undirected graph in each bag that represents contextual relationships among instances. Secondly, by introducing a novel energy function, a heuristic optimization algorithm based on Fuzzy Support Vector Machine (FSVM) is given out to find the optimal classifier on CMIL. Finally, the CMIL is applied to recognize horror images. Experimental results on an image set collected from the Internet show that the proposed method is effective on horror image recognition.
Recognizing and Filtering Web Images Based on People's Existence

Judging whether a Web image contains people is useful in both pornographic image recognition and image filtering when searching for images of people. We proposed an approximate but rapid method to solve this problem. For a Web image, three types of probabilities are calculated from the image itself, the imagepsilas associated texts and the title of the Web page is located, respectively. Then a final probability representing the peoplepsilas existence is achieved by fusion of the three probabilistic values. Based on the probability of peoplepsilas existence, we proposed a two-layer framework for pornographic image recognition and a solution of image retrieval respectively. In the experiments conducted, our proposed framework and solution demonstrate good performances in the image recognition and filtering respectively.
Dual Sticky Hierarchical Dirichlet Process Hidden Markov Model and Its Application to Natural Language Description of Motions

In this paper, a new nonparametric Bayesian model called the dual sticky hierarchical Dirichlet process hidden Markov model (HDP-HMM) is proposed for mining activities from a collection of time series data such as trajectories. All the time series data are clustered. Each cluster of time series data, corresponding to a motion pattern, is modeled by an HMM. Our model postulates a set of HMMs that share a common set of states (topics in an analogy with topic models for document processing), but have unique transition distributions. The number of HMMs and the number of topics are both automatically determined. The sticky prior avoids redundant states and makes our HDP-HMM more effective to model multimodal observations. For the application to motion trajectory modeling, topics correspond to motion activities. The learnt topics are clustered into atomic activities which are assigned predicates. We propose a Bayesian inference method to decompose a given trajectory into a sequence of atomic activities. The sources and sinks in the scene are learnt by clustering endpoints (origins and destinations) of trajectories. The semantic motion regions are learnt using the points in trajectories. On combining the learnt sources and sinks, the learnt semantic motion regions, and the learnt sequence of atomic activities, the action represented by a trajectory can be described in natural language in as automatic a way as possible. The effectiveness of our dual sticky HDP-HMM is validated on several trajectory datasets. The effectiveness of the natural language descriptions for motions is demonstrated on the vehicle trajectories extracted from a traffic scene.
Efficient block-division model for robust multiple object tracking

Tracking multiple objects under occlusion is one of the most challenging issues in computer vision. Occlusion results in mistaken match when finding the most similar candidate. Adapting to the change of objects is essential for tracking as objects often undergo intrinsic changes, but noise is unavoidably introduced during updating of the object, and this further confuses the tracker. In order to address these problems, a block-division appearance model is introduced to efficiently handle occlusion. In this model, spatial information is introduced to avoid the mistaken match between object and candidate. Based on this model, a selective updating strategy is proposed to incrementally learn the change of the object, avoiding introducing noise when updating. At the same time occlusion is deduced by monitoring the variation of each block. Experimental results in various videos validate the effectiveness of our algorithm in tracking multiple objects under occlusion.
Local Subspace Collaborative Tracking

Subspace models have been widely used for appearance based object tracking. Most existing subspace based trackers employ a linear subspace to represent object appearances, which are not accurate enough to model large variations of objects. To address this, this paper presents a local subspace collaborative tracking method for robust visual tracking, where multiple linear and nonlinear subspaces are learned to better model the nonlinear relationship of object appearances. First, we retain a set of key samples and compute a set of local subspaces for each key sample. Then, we construct a hyper sphere to represent the local nonlinear subspace for each key sample. The hyper sphere of one key sample passes the local key samples and also is tangent to the local linear subspace of the specific key sample. In this way, we are able to represent the nonlinear distribution of the key samples and also approximate the local linear subspace near the specific key sample, so that local distributions of the samples can be represented more accurately. Experimental results on challenging video sequences demonstrate the effectiveness of our method.
Salient Object Detection via Structured Matrix Decomposition.

Low-rank recovery models have shown potential for salient object detection, where a matrix is decomposed into a low-rank matrix representing image background and a sparse matrix identifying salient objects. Two deficiencies, however, still exist. First, previous work typically assumes the elements in the sparse matrix are mutually independent, ignoring the spatial and pattern relations of image regions. Second, when the low-rank and sparse matrices are relatively coherent, e.g., when there are similarities between the salient objects and background or when the background is complicated, it is difficult for previous models to disentangle them. To address these problems, we propose a novel structured matrix decomposition model with two structural regularizations: (1) a tree-structured sparsity-inducing regularization that captures the image structure and enforces patches from the same object to have similar saliency values, and (2) a Laplacian regularization that enlarges the gaps between salient objects and the background in feature space. Furthermore, high-level priors are integrated to guide the matrix decomposition and boost the detection. We evaluate our model for salient object detection on five challenging datasets including single object, multiple objects and complex scene images, and show competitive results as compared with 24 state-of-the-art methods in terms of seven performance metrics.
The Visual Object Tracking VOT2017 Challenge Results

The Visual Object Tracking challenge VOT2017 is the fifth annual tracker benchmarking activity organized by the VOT initiative. Results of 51 trackers are presented; many are state-of-the-art published at major computer vision conferences or journals in recent years. The evaluation included the standard VOT and other popular methodologies and a new "real-time" experiment simulating a situation where a tracker processes images as if provided by a continuously running sensor. Performance of the tested trackers typically by far exceeds standard baselines. The source code for most of the trackers is publicly available from the VOT page. The VOT2017 goes beyond its predecessors by (i) improving the VOT public dataset and introducing a separate VOT2017 sequestered dataset, (ii) introducing a realtime tracking experiment and (iii) releasing a redesigned toolkit that supports complex experiments. The dataset, the evaluation kit and the results are publicly available at the challenge website1.
Saliency Driven Nonlinear Diffusion Filtering for Object Recognition

We propose the saliency driven nonlinear diffusion filtering as a boost for object recognition. Taking saliency image as mask for magnitudes of gradients, nonlinear diffusion filtering treats foreground and background selectively. It preserves foreground information while filters out background information as much as possible. In salient area, semantically important structures are well preserved, while in non-salient area, cluttered structures are inhibited and smoothed into plain regions. Object recognition is conducted utilizing Bag-of-Words model, which can implicitly emphasize important foreground features for the reason of selective filtering. Experiments show that recognition accuracies using filtered images are generally higher than those using initial images, and are comparable with state-of-the-art. Consequently, we draw a safe conclusion that saliency driven nonlinear diffusion filtering undoubtedly help improve recognition performance, as long as saliency images are appropriate.
Learning human actions with an adaptive codebook

Learning a compact and yet discriminative codebook for classifying human actions is a challenging problem. One difficulty lies in that the learning procedure is split into two independent phases (dimension reduction and clustering) and thus results in the loss of discriminative information which clustering requires. Besides, traditional used principal component analysis is not optimized for class separability and may not help to improve data separation. In this paper, we propose a novel optimization framework which unifies dimension reduction and clustering. In contrast to previous methods, our method enables to dynamically select indispensable and crucial dimensions for building a discriminative codebook. We add metric learning before clustering to provide the clustering method with an optimized distance metric. Experimental results show that our approach constructs a highly discriminative codebook and achieves comparable results to other state-of-the-art approaches.
Web sensitive text filtering by combining semantics and statistics

Web sensitive information is defined as texts, pictures and other forms of information which contain erotic content on Web. How to filter this harmful information attracts researchers' interests. In order to keep Web content safe, governments have also given great support on the research on this problem. This paper first briefly review recent developments in Web sensitive information filtering then the statistic and semantic features of sensitive texts are analyzed and represented by a CNN-like word net. Finally a novel method which combines semantics and statistics is proposed to filter sensitive text on Web. Experimental results have demonstrated the proposed method's promising performance.
Context-Aware Multi-instance Learning Based on Hierarchical Sparse Representation

Multi-instance learning (MIL), a variant of supervised learning framework, has been applied in many applications. More recently, researchers focus on two important issues for MIL: Instances' contextual structures representation in the same bag and online MIL schemes. In this paper, we present an effective context-aware multi-instance learning technique using a hierarchical sparse representation (HSR-MIL) that addresses the two challenges simultaneously. We firstly construct the inner contextual structure among instances in the same bag based on a novel sparse $\varepsilon$-graph. We then propose a graph kernel based sparse bag classifier through a modified kernel sparse coding in higher-dimension feature space. At last, the HSR-MIL approach is extended to achieve online learning manner with an incremental kernel matrix update scheme. The experiments on several data sets demonstrate that our method has better performances and online learning ability.
Nonparametric Motion Feature for Key Frame Extraction in Sports Video

Key frames extraction play an important role in video abstraction. Traditional key frame extraction methods only use color, texture, or shape features to represent a frame, while the motion feature is ignored or inappropriately modeled. Since the motion feature contains a lot of semantic information in video analysis, we propose a compact representation of the dominant motion information for each frame, based on a mean shift analysis procedure. Then, an EMD (Earth mover's distance) is employed as a similarity metric for the represented motion feature. Moreover, we propose a novel temporal k-means clustering algorithm for the key frame extraction, which naturally incorporates the sequential constraint into extracted key frames. Experimental results demonstrate the effectiveness of our approach.
Online Adaboost-Based Parameterized Methods for Dynamic Distributed Network Intrusion Detection

Current network intrusion detection systems lack adaptability to the frequently changing network environments. Furthermore, intrusion detection in the new distributed architectures is now a major requirement. In this paper, we propose two online Adaboost-based intrusion detection algorithms. In the first algorithm, a traditional online Adaboost process is used where decision stumps are used as weak classifiers. In the second algorithm, an improved online Adaboost process is proposed, and online Gaussian mixture models (GMMs) are used as weak classifiers. We further propose a distributed intrusion detection framework, in which a local parameterized detection model is constructed in each node using the online Adaboost algorithm. A global detection model is constructed in each node by combining the local parametric models using a small number of samples in the node. This combination is achieved using an algorithm based on particle swarm optimization (PSO) and support vector machines. The global model in each node is used to detect intrusions. Experimental results show that the improved online Adaboost process with GMMs obtains a higher detection rate and a lower false alarm rate than the traditional online Adaboost process that uses decision stumps. Both the algorithms outperform existing intrusion detection algorithms. It is also shown that our PSO, and SVM-based algorithm effectively combines the local detection models into the global model in each node; the global model in a node can handle the intrusion types that are found in other nodes, without sharing the samples of these intrusion types.