nils-herrmann/clean_scirepeval huggingface dataset:
A Diffusion and Clustering-Based Approach for Finding Coherent Motions and Understanding Crowd Scenes.

This paper addresses the problem of detecting coherent motions in crowd scenes and presents its two applications in crowd scene understanding: semantic region detection and recurrent activity mining. It processes input motion fields (e.g., optical flow fields) and produces a coherent motion field named thermal energy field. The thermal energy field is able to capture both motion correlation among particles and the motion trends of individual particles, which are helpful to discover coherency among them. We further introduce a two-step clustering process to construct stable semantic regions from the extracted time-varying coherent motions. These semantic regions can be used to recognize pre-defined activities in crowd scenes. Finally, we introduce a cluster-and-merge process, which automatically discovers recurrent activities in crowd scenes by clustering and merging the extracted coherent motions. Experiments on various videos demonstrate the effectiveness of our approach.
A new network-based algorithm for multi-camera abnormal activity detection

In this paper, a new abnormal activity detection algorithm is proposed for multi-camera surveillance applications. The proposed algorithm models the entire scene covered by the multi-camera system as a network. In this network, each node corresponds to a segmentation of the entire scene and each edge represents the activity correlation between the corresponding segmentations. Based on this network, the proposed algorithm further models human activities as the signal transmission process in the network. Thus, abnormal activities can be detected if their ‘network transmission energy’ is obviously larger than the normal case. Compared with the previous methods, the proposed algorithm is more general and is flexible to handle various multi-camera scenarios and configurations. Experimental results demonstrate the effectiveness of the proposed algorithm.
Achievable Degrees of Freedom of MIMO Y Channel With Delayed CSIT

In this letter, we study the degrees of freedom (DOF) for multiple-input-multiple-output (MIMO) Y channel in frequency division duplex (FDD) systems. Assuming each node has delayed channel state information at the transmitter (CSIT), we propose a physical layer network coding multi-stage transmission (PLNC-MST) scheme during broadcast (BC) phase. The results show that PLNC offers significant DOF gain in MIMO Y channel.
A New Algorithm for Inferring User Search Goals with Feedback Sessions

For a broad-topic and ambiguous query, different users may have different search goals when they submit it to a search engine. The inference and analysis of user search goals can be very useful in improving search engine relevance and user experience. In this paper, we propose a novel approach to infer user search goals by analyzing search engine query logs. First, we propose a framework to discover different user search goals for a query by clustering the proposed feedback sessions. Feedback sessions are constructed from user click-through logs and can efficiently reflect the information needs of users. Second, we propose a novel approach to generate pseudo-documents to better represent the feedback sessions for clustering. Finally, we propose a new criterion )“Classified Average Precision (CAP)” to evaluate the performance of inferring user search goals. Experimental results are presented using user click-through logs from a commercial search engine to validate the effectiveness of our proposed methods.
Extracting Recurrent Motion Flows from Crowded Scene Videos: A Coherent Motion-Based Approach

In this paper, we propose a new approach which utilizes coherent motion regions to extract and visualize recurrent motion flows in crowded scene surveillance videos. The proposed approach first extract coherent motion regions from a crowded scene video. Then a frame-level clustering process is proposed to cluster frames into different recurrent-motion-pattern (RMP) groups according to the coherent-region similarity between frames. By merging similar coherent regions from the same RMP group, we can achieve motion flow regions representing the major motion flows in each recurrent motion pattern. Finally, a flow curve extraction process is also proposed which extracts flow curves from motion flow regions to provide a proper visualization of the recurrent motion patterns. Experimental results demonstrate that our approach can precisely achieve recurrent motion flows for various crowded scene videos.
A Computation Control Motion Estimation Method for Complexity-Scalable Video Coding

In this paper, a new computation-control motion estimation (CCME) method is proposed which can perform motion estimation (ME) adaptively under different computation or power budgets while keeping high coding performance. We first propose a new class-based method to measure the macroblock (MB) importance where MBs are classified into different classes and their importance is measured by combining their class information as well as their initial matching cost information. Based on the new MB importance measure, a complete CCME framework is then proposed to allocate computation for ME. The proposed method performs ME in a one-pass flow. Experimental results demonstrate that the proposed method can allocate computation more accurately than previous methods and, thus, has better performance under the same computation budget.
A Tube-and-Droplet-Based Approach for Representing and Analyzing Motion Trajectories

Trajectory analysis is essential in many applications. In this paper, we address the problem of representing motion trajectories in a highly informative way, and consequently utilize it for analyzing trajectories. Our approach first leverages the complete information from given trajectories to construct a thermal transfer field which provides a context-rich way to describe the global motion pattern in a scene. Then, a 3D tube is derived which depicts an input trajectory by integrating its surrounding motion patterns contained in the thermal transfer field. The 3D tube effectively: 1) maintains the movement information of a trajectory, 2) embeds the complete contextual motion pattern around a trajectory, 3) visualizes information about a trajectory in a clear and unified way. We further introduce a droplet-based process. It derives a droplet vector from a 3D tube, so as to characterize the high-dimensional 3D tube information in a simple but effective way. Finally, we apply our tube-and-droplet representation to trajectory analysis applications including trajectory clustering, trajectory classification & abnormality detection, and 3D action recognition. Experimental comparisons with state-of-the-art algorithms demonstrate the effectiveness of our approach.
Multiscale Semilocal Interpolation With Antialiasing

Aliasing is a common artifact in low-resolution (LR) images generated by a downsampling process. Recovering the original high-resolution image from its LR counterpart while at the same time removing the aliasing artifacts is a challenging image interpolation problem. Since a natural image normally contains redundant similar patches, the values of missing pixels can be available at texture-relevant LR pixels. Based on this, we propose an iterative multiscale semilocal interpolation method that can effectively address the aliasing problem. The proposed method estimates each missing pixel from a set of texture-relevant semilocal LR pixels with the texture similarity iteratively measured from a sequence of patches of varying sizes. Specifically, in each iteration, top texture-relevant LR pixels are used to construct a data fidelity term in a maximum a posteriori estimation, and a bilateral total variation is used as the regularization term. Experimental results compared with existing interpolation methods demonstrate that our method can not only substantially alleviate the aliasing problem but also produce better results across a wide range of scenes both in terms of quantitative evaluation and subjective visual quality.
An efficient coding method for coding Region-of-Interest locations in AVS2

Region-of-Interest (ROI) location information in videos has many practical usages in video coding field, such as video content analysis and user experience improvement. Although ROI-based coding has been studied widely by many researchers to improve coding efficiency for video contents, the ROI location information itself is seldom coded in video bitstream. In this paper, we will introduce our proposed ROI location coding tool which has been adopted in surveillance profile of AVS2 video coding standard (surveillance profile). Our tool includes three schemes: direct-coding scheme, differential- coding scheme, and reconstructed-coding scheme. We will illustrate the details of these schemes, and perform analysis of their advantages and disadvantages, respectively.
Towards Good Practices for Action Video Encoding

High dimensional representations such as VLAD or FV have shown excellent accuracy in action recognition. This paper shows that a proper encoding built upon VLAD can achieve further accuracy boost with only negligible computational cost. We empirically evaluated various VLAD improvement technologies to determine good practices in VLAD-based video encoding. Furthermore, we propose an interpretation that VLAD is a maximum entropy linear feature learning process. Combining this new perspective with observed VLAD data distribution properties, we propose a simple, lightweight, but powerful bimodal encoding method. Evaluated on 3 benchmark action recognition datasets (UCF101, HMDB51 and Youtube), the bimodal encoding improves VLAD by large margins in action recognition.
An efficient framework for recognizing traffic lights in night traffic images

In this paper, we propose a new and efficient framework for detecting and recognizing traffic signal lights with the corresponding countdown counters in the night scenes. The proposed framework first compares the difference between the daytime and night situation of traffic lights in the image and locate the recognition candidates in the HLC color space. With the locating of the main location marks, the shooting angle direction could be corrected. Based on the frontal direction, the content of the traffic lights can be recognized with the template matching measure. Furthermore, in order to reduce computation, a complexity reducing method is proposed. Experimental results on various scenes demonstrate the effectiveness of our proposed framework.
Good Practices for Learning to Recognize Actions Using FV and VLAD

High dimensional representations such as Fisher vectors (FV) and vectors of locally aggregated descriptors (VLAD) have shown state-of-the-art accuracy for action recognition in videos. The high dimensionality, on the other hand, also causes computational difficulties when scaling up to large-scale video data. This paper makes three lines of contributions to learning to recognize actions using high dimensional representations. First, we reviewed several existing techniques that improve upon FV or VLAD in image classification, and performed extensive empirical evaluations to assess their applicability for action recognition. Our analyses of these empirical results show that normality and bimodality are essential to achieve high accuracy. Second, we proposed a new pooling strategy for VLAD and three simple, efficient, and effective transformations for both FV and VLAD. Both proposed methods have shown higher accuracy than the original FV/VLAD method in extensive evaluations. Third, we proposed and evaluated new feature selection and compression methods for the FV and VLAD representations. This strategy uses only 4% of the storage of the original representation, but achieves comparable or even higher accuracy. Based on these contributions, we recommend a set of good practices for action recognition in videos for practitioners in this field.
Fast sub-pixel motion estimation and mode decision for H.264

Motion estimation (ME) is one of the most time-consuming parts in video coding. The use of multiple partition sizes in H.264 makes the ME even more complicated. It is important to develop fast sub-pixel ME algorithms due to (1) The computation overhead by sub-pixel ME has become relatively significant while the complexity of integer-pel search has been greatly reduced by fast algorithms and (2) Reducing sub-pel searching points can save the computation for interpolating sub-pixel values. In this paper, a new fast sub-pixel ME algorithm is proposed which performs a 'rough' sub-pel search before the partition selection and only performs the 'precise' sub-pel search for the best partition. Experimental results show that our method can reduce the sub-pel search points by more than 50% compared to existing fast sub-pel ME methods with little quality degradation.
Tree-Based Visualization and Optimization for Image Collection

The visualization of an image collection is the process of displaying a collection of images on a screen under some specific layout requirements. This paper focuses on an important problem that is not well addressed by the previous methods: visualizing image collections into arbitrary layout shapes while arranging images according to user-defined semantic or visual correlations (e.g., color or object category). To this end, we first propose a property-based tree construction scheme to organize images of a collection into a tree structure according to user-defined properties. In this way, images can be adaptively placed with the desired semantic or visual correlations in the final visualization layout. Then, we design a two-step visualization optimization scheme to further optimize image layouts. As a result, multiple layout effects including layout shape and image overlap ratio can be effectively controlled to guarantee a satisfactory visualization. Finally, we also propose a tree-transfer scheme such that visualization layouts can be adaptively changed when users select different “images of interest.” We demonstrate the effectiveness of our proposed approach through the comparisons with state-of-the-art visualization techniques.
Survey on blind image forgery detection

With the mushroom growth of state-of-the-art digital image and video manipulations tools, establishing the authenticity of multimedia content has become a challenging issue. Digital image forensics is an increasingly growing research field that symbolises a never ending struggle against forgery and tampering. This survey attempts to cover the blind techniques that have been proposed for exposing forgeries. This work dwells on the detection techniques for three of the most common forgery types, namely copy/move, splicing and retouching.
A new Local-Main-Gradient-Orientation HOG and contour differences based algorithm for object classification

This paper presents a new algorithm to better classify objects in videos. In our case, the objects are cars, vans, and people on the roads. First, in order to extract the moving objects more precisely, we have proposed a method for foreground extraction based on the contour differences between the video frame and the background image. Second, after we got the integrated moving object, we have proposed a new algorithm to extract better features from the object. The new algorithm is based on two extended Histogram of Oriented Gradient (HOG) descriptor. We have improved HOG in two aspects: (a) selecting the gradient information from the moving objects and discarding the background gradient; (b) weighting every bin of gradient orientation histogram according to their significance within predefined area, in order to emphasize the important gradient information. We obtained Contour-Difference HOG (CD-HOG) from the first extension and Local-Main-Gradient-Orientation HOG (LMGO-HOG) from the second extended HOG. These extensions can cope with the cluttered background and make the features more distinguishable. Each of the extended HOG descriptors can produce a satisfying performance separately and an even better one if they are applied in cascade. From extensive evaluations, we showed the wonderful performance of our algorithm, and the accuracy rate of 94.04% can be achieved in some cases.
Group event detection for video surveillance

This paper presents a novel approach for automatic recognition of group activities for video surveillance applications. We propose to use a group representative to handle the recognition with flexible or varying number of group members, and use an Asynchronous Hidden Markov Model (AHMM) to model the relationship between two people. Furthermore, we propose a group activity detection algorithm which can handle symmetric and asymmetric group activities, and demonstrate that this approach enables the detection of hierarchical interactions between people. Experimental results show the effectiveness of our approach.
A new one-pass Complexity-Scalable computation-control method for video coding

In this paper, a new Complexity-Scalable computation control method is proposed which can perform Motion Estimation (ME) adaptively under different computation or power budgets while keeping high coding performance. We first propose a new class-based method to measure the Macroblock (MB) importance where MBs are classified into different classes and their importance is measured by combining their class information as well as their initial matching cost information in the ME. Based on the new MB importance measure, a computation control framework is then proposed to allocate computation for ME. Experimental results demonstrate that the proposed method can allocate computation more accurately than previous methods and thus has better performance under the same computation budget.
Activity Recognition Using a Combination of Category Components and Local Models for Video Surveillance

This paper presents a novel approach for automatic recognition of human activities for video surveillance applications. We propose to represent an activity by a combination of category components and demonstrate that this approach offers flexibility to add new activities to the system and an ability to deal with the problem of building models for activities lacking training data. For improving the recognition accuracy, a confident-frame-based recognition algorithm is also proposed, where the video frames with high confidence for recognizing an activity are used as a specialized local model to help classify the remainder of the video frames. Experimental results show the effectiveness of the proposed approach.
Periodic motion detection with ROI-based similarity measure and extrema-based reference selection

This paper presents a new algorithm for detecting and analyzing the periodic motions in video sequences. Different from the previous methods which detect periodic motions from the entire frame, we propose a convexhull- based process to automatically determine the regions of interest (ROI) of the motions and utilize an ROI-based similarity measure to detect the motion periods. Furthermore, we also propose an extrema-based method to select the optimal reference frame for further improving the periodic detection performance. Our proposed algorithm can not only effectively detect motion periods with both constant and variable period lengths, but also have obvious advantage when handling periodic motion with slight movements. Experimental results demonstrate the effectiveness of our proposed method.
Traffic flow matching with clique and triplet cues

This paper addresses a new problem of matching traffic-flow patterns from different scenes. We firstly introduce cliques to measure the topology similarity between traffic flow patterns. Based on the clique information, a matching cost function is formulated to find the optimal flow-pattern matching. In order to avoid wrong matches due to large variations in traffic flow distributions, we further introduce triplets to measure the flow-wise correlation in a scene and include them into the matching cost function. Thus, constraints of traffic flows' relative position can be suitably considered during the flow-pattern matching process. Finally, a random-walk-based graph matching method is also utilized to efficiently solve the matching cost function optimization problem. Experimental results on both simulated flow data and real flow data demonstrate the effectiveness of our approach.
A patch-based framework for detecting abnormal activities with a PTZ camera

In this paper, a novel patch-based (PB) framework is proposed for detecting abnormal activities using a Pan-Tilt-Zoom (PTZ) camera. We first propose a new scene-patch-based (SSB) algorithm which can efficiently extract the target object's global trajectory from the PTZ camera. Furthermore, we propose an extended network-based (ENB) algorithm for detecting abnormal activities. The proposed ENB algorithm models the entire scene as a network where each node in the network corresponds to a patch of the scene and each edge between nodes corresponds to the activity correlation between the scene patchs. Based on this network, a recursive training strategy is proposed to train the edge weights in the network such that abnormal activities can be effectively detected through these trained edge weights. Experimental results demonstrate the effectiveness of our proposed framework.
A new class-based early termination method for fast motion estimation in video coding

Motion Estimation (ME) is one of the most time-consuming parts in video coding. It is always desirable to develop fast ME algorithms to reduce the ME complexity. In this paper, a new early termination method is proposed for fast motion estimation. The proposed method first classifies each Macroblock into one of three classes based on the estimation of the possible matching cost improvement from future search points. Different early termination strategies are then applied to different classes. Experimental results show that the proposed method can significantly reduce the search points with little quality degradation.
A real-time virtual dressing system with RGB-D camera

As RGB-D cameras become cheaper commodities, many human-computer interaction applications are facilitated, such as human-machine interface (HMI) and virtual reality (VR). In this paper, we focus on virtual dressing, which is a newly heated application of human-computer interaction, and develop a novel framework for real-time virtual dressing. We made three major contributions in our framework: 1) We introduce an effective algorithm to pre-process and improve the skeleton data from an RGB-D camera; 2) We develop a strategy to measure the skeleton motion parameters (scaling, translation and rotation) and apply a two-step deforming scheme specific to create dress deformation results. 3) We introduce hand-integration module to integrate the clothing models and user arms, so as to guarantee user arms being properly displayed in the visual dressing result. Experimental results show that our proposed virtual dressing system achieves satisfactory visual dressing results.