nils-herrmann/clean_scirepeval huggingface dataset:
Mixtures of von Mises Distributions for People Trajectory Shape Analysis

People trajectory analysis is a recurrent task in many pattern recognition applications, such as surveillance, behavior analysis, video annotation, and many others. In this paper, we propose a new framework for analyzing trajectory shape, invariant to spatial shifts of the people motion in the scene. In order to cope with the noise and the uncertainty of the trajectory samples, we propose to describe the trajectories as a sequence of angles modeled by distributions of circular statistics, i.e., a mixture of von Mises (MovM) distributions. To deal with MovM, we define a new specific expectation-maximization (EM) algorithm for estimating the parameters and derive a closed form of the Bhattacharyya distance between single von Mises pdfs. Trajectories are then modeled with a sequence of symbols, corresponding to the most suitable distribution in the mixture, and compared each other after a global alignment procedure to cope with trajectories of different lengths. The trajectories in the training set are clustered according to their shape similarity in an off-line phase, and testing trajectories are then classified with a specific on-line EM, based on sufficient statistics. The approach is particularly suitable for classifying people trajectories in video surveillance, searching for abnormal (i.e., infrequent) paths. Tests on synthetic and real data are provided with also a complete comparison with other circular statistical and alignment methods.
Learning to identify leaders in crowd

Leader identification is a crucial task in social analysis, crowd management and emergency planning. In this paper, we investigate a computational model for the individuation of leaders in crowded scenes. We deal with the lack of a formal definition of leadership by learning, in a supervised fashion, a metric space based exclusively on people spatiotemporal information. Based on Tarde's work on crowd psychology, individuals are modeled as nodes of a directed graph and leaders inherits their relevance thanks to other members references. We note this is analogous to the way websites are ranked by the PageRank algorithm. During experiments, we observed different feature weights depending on the specific type of crowd, highlighting the impossibility to provide a unique interpretation of leadership. To our knowledge, this is the first attempt to study leader identification as a metric learning problem.
Segmentation of moving objects at frame rate: a dedicated hardware solution

Many works in image processing concern segmentation of moving objects in sequence of images. This problem is particularly critical, since it represents the first step of many complex processes of computer vision, for applications like object tracking, video-surveillance, monitoring, and autonomous navigation. In such applications, both real-time and low-cost requirements should be satisfied. To this aim we propose a dedicated hardware solution, based on reconfigurable logic, that provides motion detection and moving objects segmentation at frame rate.
Kernelized Structural Classification for 3D Dogs Body Parts Detection

Despite pattern recognition methods for human behavioral analysis has flourished in the last decade, animal behavioral analysis has been almost neglected. Those few approaches are mostly focused on preserving livestock economic value while attention on the welfare of companion animals, like dogs, is now emerging as a social need. In this work, following the analogy with human behavior recognition, we propose a system for recognizing body parts of dogs kept in pens. We decide to adopt both 2D and 3D features in order to obtain a rich description of the dog model. Images are acquired using the Microsoft Kinect to capture the depth map images of the dog. Upon depth maps a Structural Support Vector Machine (SSVM) is employed to identify the body parts using both 3D features and 2D images. The proposal relies on a kernelized discriminative structural classificator specifically tailored for dogs independently from the size and breed. The classification is performed in an online fashion using the LaRank optimization technique to obtaining real time performances. Promising results have emerged during the experimental evaluation carried out at a dog shelter, managed by IZSAM, in Teramo, Italy.
Gesture Recognition in Ego-centric Videos Using Dense Trajectories and Hand Segmentation

We present a novel method for monocular hand gesture recognition in ego-vision scenarios that deals with static and dynamic gestures and can achieve high accuracy results using a few positive samples. Specifically, we use and extend the dense trajectories approach that has been successfully introduced for action recognition. Dense features are extracted around regions selected by a new hand segmentation technique that integrates superpixel classification, temporal and spatial coherence. We extensively testour gesture recognition and segmentation algorithms on public datasets and propose a new dataset shot with a wearable camera. In addition, we demonstrate that our solution can work in near real-time on a wearable device.
A deep multi-level network for saliency prediction

This paper presents a novel deep architecture for saliency prediction. Current state of the art models for saliency prediction employ Fully Convolutional networks that perform a non-linear combination of features extracted from the last convolutional layer to predict saliency maps. We propose an architecture which, instead, combines features extracted at different levels of a Convolutional Neural Network (CNN). Our model is composed of three main blocks: a feature extraction CNN, a feature encoding network, that weights low and high level feature maps, and a prior learning network. We compare our solution with state of the art saliency models on two public benchmarks datasets. Results show that our model outperforms under all evaluation metrics on the SALICON dataset, which is currently the largest public dataset for saliency prediction, and achieves competitive results on the MIT300 benchmark. Code is available at https://github.com/marcellacornia/mlnet.
MPEG-7 compliant shot detection in sport videos

In this paper we propose a system for automatic detection of shots in sport videos. Our work covers two main aspects: the first is robust shot detection in presence of fast object motion and camera operations. To this aim we propose a new algorithm, unique for both cuts and linear transitions detection, which only needs the tuning of two parameters. An extended comparison with four transition detection algorithms, representing the state of the art in literature, is reported. Examples with formula 1, basket, soccer and cycling videos are analyzed. The second aspect is an in depth discussion on the annotation of shots and transitions with the MPEG-7 standard.
A Mobile Vision System for Fast and Accurate Ellipse Detection

Several papers addressed ellipse detection as a first step for several computer vision applications, but most of the proposed solutions are too slow to be applied in real time on large images or with limited hardware resources, as in the case of mobile devices. This demo is based on a novel algorithm for fast and accurate ellipse detection. The proposed algorithm relies on a careful selection of arcs which are candidate to form ellipses and on the use of Hough transform to estimate parameters in a decomposed space. The demo will show it working on a commercial smart-phone.
Improving shadow suppression in moving object detection with HSV color information

Video-surveillance and traffic analysis systems can be heavily improved using vision-based techniques able to extract, manage and track objects in the scene. However, problems arise due to shadows. In particular, moving shadows can affect the correct localization, measurements and detection of moving objects. This work aims to present a technique for shadow detection and suppression used in a system for moving visual object detection and tracking. The major novelty of the shadow detection technique is the analysis carried out in the HSV color space to improve the accuracy in detecting shadows. Signal processing and optic motivations of the approach proposed are described. The integration and exploitation of the shadow detection module into the system are outlined and experimental results are shown and evaluated.
Multistage Particle Windows for Fast and Accurate Object Detection

The common paradigm employed for object detection is the sliding window (SW) search. This approach generates grid-distributed patches, at all possible positions and sizes, which are evaluated by a binary classifier: The tradeoff between computational burden and detection accuracy is the real critical point of sliding windows; several methods have been proposed to speed up the search such as adding complementary features. We propose a paradigm that differs from any previous approach since it casts object detection into a statistical-based search using a Monte Carlo sampling for estimating the likelihood density function with Gaussian kernels. The estimation relies on a multistage strategy where the proposal distribution is progressively refined by taking into account the feedback of the classifiers. The method can be easily plugged into a Bayesian-recursive framework to exploit the temporal coherency of the target objects in videos. Several tests on pedestrian and face detection, both on images and videos, with different types of classifiers (cascade of boosted classifiers, soft cascades, and SVM) and features (covariance matrices, Haar-like features, integral channel features, and histogram of oriented gradients) demonstrate that the proposed method provides higher detection rates and accuracy as well as a lower computational burden w.r.t. sliding window detection.
Trustworthiness of Acquaintance peers on Access Control Models

In a fully distributed peer-to-peer (P2P) overlay network, peer processes (peers) cannot perceive where every object exists due to the scalability and openness. A peer has to obtain service through communicating with acquaintance peers. Here, acquaintance peers may hold obsolete information and may be even malicious. Hence, it is critical for a peer to identify which acquaintance is more trustworthy. In this paper, the trustworthiness of an acquaintance is defined in terms of the satisfiability and ranking factor. We define how satisfiable a requesting peer is for each type of access requests by newly taking into account the authorization. The ranking factor of an acquaintance shows how much the acquaintance is trusted by trustworthy acquaintances. The trustworthiness of each acquaintance is propagated in a peer-by-peer way. We evaluate how the ranking factors of acquaintance is changing through interactions among peers.
A real-time embedded solution for skew correction in banknote analysis

Several industrial applications do require embedded solutions both for compacting the hardware occupation and reducing energy consumption, and for achieving high speed performance. This paper presents a computer vision system developed for correcting image skew in applications for banknote analysis and classification. The system must be very efficient and run on a fixed-point DSP with limited computational resources. Consequently, we propose three innovative improvements to basic and general-purpose image processing techniques that can be helpful in other computer vision applications on embedded devices. In particular, we address: a) an efficient labeling with an union-find approach for hole filling, b) a fast Hough transform implementation, and c) a very high-speed estimation of affine transformation for skew correction. The reported results demonstrate both the accuracy and the efficiency of the system, also in presence of severe skew. In terms of efficiency, the computational time is reduced of about two orders of magnitude.
An Open Source Architecture for Low-Latency Video Streaming on PDAs

This paper presents a open-source system for low- latency video streaming on PDAs, specifically addressing mobile video surveillance requirements. The system is based on H.264 and suitably modified to obtain the best trade-off between image quality and video fluidity, working also at very limited bandwidths. Moreover, the used controls allow to keep the number of lost frames very low. A large set of experiments and comparisons have been carried out and the achieved results demonstrate the efficacy and efficiency of our system.
The vector-gradient Hough transform for identifying straight-translation generated shapes

The paper introduces the vector-gradient Hough transform (VGHT), a modified version of the gradient weighted Hough transform (GWHT), defined in vector space and able to exploit all the vector information of the gradient of luminosity. The new formulation, directly derived from the Radon transform, is analyzed and compared with the GWHT, in order to point out the improvement in selectivity provided by the VGHT in a strictly polar parametric space, without any relevant increase in computational complexity. This approach can be very suitable for identifying a specifically defined model of shapes in gray level images, ideally generated by a translation in the 2D space of a 1D luminosity profile. Finally, the suitability of the VGHT in real applications is shown with examples in the area of defect identification for automated visual inspection.
Improvement in range segmentation parameters tuning

A great effort has been done during last years to improve range image segmentation results. The efficacy of the algorithms is affected by the parameters turning. In this work two well-known search techniques have been applied to this task: genetic algorithms and simulated annealing. these techinques are adopted in cascade: the former to obtain a rough seed point set and the latter to have a more precise refinement of suitable solutions. We addresses our efforts towards the range segmenter proposed by the University of Bern, that seems to be the best in terms of versatility, being able to segment planar and curved surfaces, and in term of speed and quality of the performed segmentations.
Multi-view people surveillance using 3D information

In this paper we introduce a novel surveillance system, which uses 3D information extracted from multiple cameras to detect, track and re-identify people. The detection method is based on a 3D Marked Point Process model using two pixel-level features extracted from multi-plane projections of binary foreground masks, and uses a stochastic optimization framework to estimate the position and the height of each person. We apply a rule based Kalman-filter tracking on the detection results to find the object-to-object correspondence between consecutive time steps. Finally, a 3D body model based long-term tracking module connects broken tracks and is also used to re-identify people.
People trajectory mining with statistical pattern recognition

People social interaction analysis is a complex and interesting problem that can be faced from several points of view depending on the application context. In videosurveillance contexts many indicators of people habits and relations exist and, among these, people trajectories analysis can reveal many aspects of the way people behave in social environments. We propose a statistical framework for trajectories mining that analyzes, in an integrated solution, several aspects of the trajectories such as location, shape and speed properties. Three different models are proposed to deal with non-idealities of the selected features in conjunction with a robust inexact- matching similarity measure for comparing sequences with different lengths. Experimental results in a real scenario demonstrates the efficacy of the framework in clustering people trajectories with the purpose of analyze frequent behaviors in complex environments.
A people counting system for business analytics

This paper deals with people counting in stores for business analytics using stereo vision. Among the several problems in this type of applications, two are the most relevant for our purposes: the management of occlusions and the distinction between adult people (potential customers) and other objects (children, trolleys, strollers, animals, etc.). The proposed solution uses a novel approach for object detection (based on background suppression on a so-called “depth bird-eye view” and the clustering on the 3D point cloud by means of mean shift with a cylindrical kernel) followed by an adult people classifier which exploits a fitness measure with respect to a cylindrical human body model. The fitness is computed using Montecarlo sampling to estimate the volume occupation. Experiments are conducted on two real setups (including a store in a normal day of activity) and compared with a previous work. The results demonstrate the accuracy of the proposed solution.
Probabilistic people tracking for occlusion handling

This work presents a novel people tracking approach, able to cope with frequent shape changes and large occlusions. In particular, the tracks are described by means of probabilistic masks and appearance models. Occlusions due to other tracks or due to background objects and false occlusions are discriminated. The tracking system is general enough to be applied with any motion segmentation module, it can track people interacting each other and it maintains the pixel assignment to track even with large occlusions. At the same time, the update model is very reactive, so as to cope with sudden body motion and silhouette's shape changes. Due to its robustness, it has been used in many experiments of people behavior control in indoor situations.
Using circular statistics for trajectory shape analysis

The analysis of patterns of movement is a crucial task for several surveillance applications, for instance to classify normal or abnormal people trajectories on the basis of their occurrence. This paper proposes to model the shape of a single trajectory as a sequence of angles described using a mixture of Von Mises (MoVM) distribution. A complete EM (expectation maximization) algorithm is derived for MoVM parameters estimation and an on-line version proposed to meet real time requirement. Maximum-A-Posteriori is used to encode the trajectory as a sequence of symbols corresponding to the MoVM components. Iterative k-medoids clustering groups trajectories in a variable number of similarity classes. The similarity is computed aligning (with dynamic programming) two sequences and considering as symbol-to-symbol distance the Bhattacharyya distance between von Mises distributions. Extensive experiments have been performed on both synthetic and real data.
Learning where to attend like a human driver

Despite the advent of autonomous cars, it's likely — at least in the near future — that human attention will still maintain a central role as a guarantee in terms of legal responsibility during the driving task. In this paper we study the dynamics of the driver's gaze and use it as a proxy to understand related attentional mechanisms. First, we build our analysis upon two questions: where and what the driver is looking at? Second, we model the driver's gaze by training a coarse-to-fine convolutional network on short sequences extracted from the DR(eye)VE dataset. Experimental comparison against different baselines reveal that the driver's gaze can indeed be learnt to some extent, despite i) being highly subjective and ii) having only one driver's gaze available for each sequence due to the irreproducibility of the scene. Eventually, we advocate for a new assisted driving paradigm which suggests to the driver, with no intervention, where she should focus her attention.
Low-Latency Live Video Streaming over Low-Capacity Networks

This paper presents an effective system for streaming over low-capacity networks (such as GPRS and EGPRS) of live videos with low latency. Existing solutions are either too complex or not suitable to our scope. For this reason, we developed a complete, ready-to-use streaming system based on H.264/AVC codec and UDP/IP stack. The system employs adaptive controls to achieve the best tradeoff between low latency and good video fluency, by keeping the UDP buffer occupancy at the decoder side between two given levels. Our experiments demonstrate that this system is able to transmit live videos at CIF format and 10 fps over GPRS/EGPRS with very low latency (1.73 sec on average, basically due to the network delay), good fluency and average quality, measured with PSNR, of 31 dB on GPRS at 23 kbps at 10 fps
Ambient intelligence for security in public parks: the LAICA project

In this paper, we address the exploitation of computer vision techniques to develop multimedia services and automatic monitoring systems related to the security and the privacy in public areas. The research is part of a two-year Italian project called LAICA, intended to provide advanced services for citizens and public officers. Citizens want fast and friendly Web access to public places, to see the environment in real-time without violating the privacy laws. Public officers and policy centres want a fast and reactive monitoring system, capable to automatically detect dangerous situations, given the huge amount of cameras that can not be monitored simultaneously by human operators. In this work, we describe the project and the defined methodologies in multicamera video mosaicing, people tracking and consistent labelling and access to processed data with face obscuration.
Optimal range segmentation parameters through genetic algorithms

A wide number of algorithms for surface segmentation in range images have been recently proposed characterized by different approaches (edge filling, region growing,...), different surface types (either for planar or curved surfaces) and different parameters involved. Optimization of the parameter set is a particularly critical task since the range of parameter variability is often quite large: parameter selection depends on surface type, sensors and the required speed which strongly of affect performance. A framework for parameter optimization is proposed based on genetic algorithms. Such algorithms allow a general approach that has been successfully applied on different state-of-the-art segmenters and different range image databases.
A fast multi-model approach for object duplicate extraction

This paper presents an innovative approach for localizing and segmenting duplicate objects for industrial applications. The working conditions are challenging, with complex heavily-occluded objects, arranged at random in the scene. To account for high flexibility and processing speed, this approach exploits SIFT keypoint extraction and mean shift clustering to efficiently partition the correspondences between the object model and the duplicates onto the different object instances. The re-projection (by means of an Euclidean transform) of some delimiting points onto the current image is used to segment the object shapes. This procedure is compared in terms of accuracy with existing homography-based solutions which make use of RANSAC to eliminate outliers in the homography estimation. Moreover, in order to improve the extraction in the case of reflective or transparent objects, multiple object models are used and fused together. Experimental results on different and challenging kinds of objects are reported.
Performance analysis of MPEG-4 decoder and encoder

A performance analysis of MPEG-4 encoder and decoder programs on a standard personal computer is presented. The paper first describes the MPEG-4 computational load and discusses related works, then outlines the performance analysis. Experimental results show that while the decoder program can be easily executed in real time, the encoder requires execution times in the order of seconds per frame which call for substantial optimisation to satisfy real-time constraints.