nils-herrmann/clean_scirepeval huggingface dataset:
Zoom on target while tracking

In this paper, the problem of continuous tracking of moving objects with a PTZ camera is addressed. In particular, the problem of tracking moving objects during zoom phases is solved by using a feature clustering technique. In order to adopt such a method, we need, first, a step where during tracking with a pan&tilt camera we can identify the mobile objects in the monitored scene. Therefore, a set of good trackable features belonging to the selected target is extracted. In this research, we adopt a feature clustering method that is able to discriminate between features associated with the background and features associated with different moving objects. As a result, for each moving object, we have a set of correctly tracked features that is used to track the objects. Experiments have been performed on outdoor environments where either people or vehicles have been tracked. The results highlight how such a technique can be included in a more complex system able to maintain targets in the field of view of the camera, and to zoom on an object of interest when desired.
An Autonomous Vehicle for Video Surveillance of Indoor Environments

In this paper, the problem of the surveillance and the security of indoor environments is addressed through the development of an autonomous surveillance vehicle (ASV). The ASV has been designed to perform, in addition to the classical robotic tasks (e.g., navigation and obstacle avoiding), the tracking of objects (e.g., persons) moving in indoor environments. The selection of the target object to be tracked can be decided by a remote operator or autonomously by the ASV itself in the case that a suspicious behavior has been detected (e.g., a person entering a forbidden area, etc.). The tracking procedure allows the ASV to maintain the interesting objects in the center of the image and in specific cases to localize particular parts of the object (e.g., face of a person, etc.) in order to recognize it. Experimental results have been performed on different real scenarios where no objects move inside the monitored scene and where a group of people move in a hallway
A Structured Committee for Food Recognition

Food recognition is an emerging computer vision topic. The problem is characterized by the absence of rigid structure of the food and by the large intra-class variations. Existing approaches tackle the problem by designing ad-hoc feature representations based on a priori knowledge of the problem. Differently from these, we propose a committee-based recognition system that chooses the optimal features out of the existing plethora of available ones (e.g., color, texture, etc.). Each committee member is an Extreme Learning Machine trained to classify food plates on the basis of a single feature type. Single member classifications are then considered by a structural Support Vector Machine to produce the final ranking of possible matches. This is achieved by filtering out the irrelevant features/classifiers, thus considering only the relevant ones. Experimental results show that the proposed system outperforms state-of-the-art works on the most used three publicly available benchmark datasets.
Image acquisition enhancement for active video surveillance

Last years have seen a big effort of research community to solve the security problems by developing video surveillance systems. Normally, developed systems have the characteristic to be executed on images acquired by autonomous cameras. In this paper, we propose a new way to develop visual surveillance systems which would not be a set of passive modules but systems that actively decide both what to see and how to see it. In particular, our idea is to act on the regulation of the acquisition parameters as consequence of what the system needs to see. The regulation strategy is based on two parameters, focus and iris, and aims to identify an optimal sequence of steps to enhance the acquisition quality of the object of interest. To this end, a hierarchy of neural networks has been employed to select first which parameter must be regulated then to adjust it.
Person Reidentification in a Distributed Camera Network Framework

Plenty of research has been conducted to obtain the best reidentification performance between a single camera-pairs. None of the current approaches has addressed the reidentification in a camera network by considering the network topology (i.e., the structure of the monitored environment). We introduce a distributed network person reidentification framework which introduces the following contributions. 1) a camera matching cost to measure the reidentification performance between nodes of the network and 2) a derivation of the distance vector algorithm which allows to learn the network topology thus to prioritize and limit the cameras inquired for the matching of the probe. Results on three benchmark datasets show that the network topology can be learned in an unsupervised fashion and network-wise reidentification performance improves. As a side effect, we obtain that the communication bandwidth usage is reduced.
Focusing on Target&#146;s Features while Tracking

Usually tracking objects by means of a moving camera means solving the problem of segmenting its motion from the background and maintain the gaze on it. Usually key points, i.e. features like corners, are matched throughout consecutive frame to register frames and detecting objects or to compute a fixation point on the object of interest. None of the methods proposed so far investigates how, more then just matching features between frames, the tuning of their quality would increase the robustness of the methods. Hence, the novelty proposed in this paper concerns the automatic tuning of the focus parameter to increase the tracking capabilities and strength the robustness of the entire system
Re-identify people in wide area camera network

Tracking individuals within a wide area camera network is a tough problem. Obtaining information across uncovered areas is an open issue that person re-identification methods deal with. A novel appearance-based method for person re-identification is proposed. The approach computes a novel discriminative signature by exploiting multiple local features. A novel signature distance measure is given by exploiting a body part division approach. The method has been compared to state-of-the-art methods using a re-identification benchmark dataset. A new dataset acquired from non-overlapping cameras has been built to validate the method against a real wide area camera network scenario. The method has proven to be robust against low resolution images, viewpoint and illumination changes, occlusions and pose variations. Results show that the proposed approach outperforms state-of-the-art methods used for comparison.
Wide-Slice Residual Networks for Food Recognition

Image-based food recognition pose new challenges for mainstream computer vision algorithms. Recent works in the field focused either on hand-crafted representations or on learning these by exploiting deep neural networks (DNN). Despite the success of DNN-based works, these exploit off-the-shelf deep architectures which are not cast to the specific food classification problem. We believe that better results can be obtained if the architecture is defined with respect to an analysis of the food composition. Following such an intuition, this work introduces a new deep scheme that is designed to handle the food structure. In particular, we focus on the vertical food traits that are common to a large number of categories (i.e., 15% of the whole data in current datasets). Towards the final objective, we first introduce a slice convolution block to capture such specific information. Then, we leverage on the recent success of deep residual blocks and combine those with the sliced convolution to produce the classification score. Extensive evaluations on three benchmark datasets demonstrated that our solution has better performance than existing approaches (e.g., a top–1 accuracy of 90.27% on the Food-101 dataset).
A robust face detection system for real environments

In this paper, a robust real-time face detection system based on the integration of different location methods is proposed. A hierarchical architecture composed of three levels is designed. At the first level, a change detection method is applied to detect blobs of moving objects (i.e., humans) in the scene. Then, the silhouette of each blob is analyzed to focalize the attention of the system on small image areas where the probability of finding human heads is high. At the second level, two different methods, i.e., the skin color and the principal component analysis, are applied to locate human faces. Finally, the higher level fuses the obtained location data to improve the face detection reliability. The system is tested in outdoor environments in the context of a video-based surveillance system.
PTZ camera network reconfiguration

Vision Network based surveillance systems are more and more common in public places. Typically, a mixture of static and Pan-Tilt-Zoom (PTZ) cameras is used. Modern systems task PTZ cameras as a consequence of particular events needing further investigation; anyhow, the configuration of the network can be considered fixed and determined at the moment of deployment. In this work, we deal with a problem that has not yet been widely addressed: how a network can automatically change its configuration to enhance the monitoring capabilities. In particular, we propose a novel network reconfiguration algorithm that, given a map of activities, configures the Pan, Tilt and Zoom parameters of all the cameras in order to improve the detection. A spherical model to project all the activities in the monitored area with respect to the optical centre of each camera is introduced. Such a model leads to an optimization problem that can be solved by means of the Expectation-Maximization algorithm and whose solutions are the new Pan, Tilt and Zoom values for each PTZ camera. Experimental results will be proposed with both synthetic and real data to show how the proposed algorithm can be applied to different cases.
A network of co-operative cameras for visual surveillance

A network of co-operative cameras for the visual surveillance of parking lots is presented. Such a network employs multiple subnets able to manage static and active cameras in a hierarchical framework. The system is able to track multiple targets simultaneously and in real-time throughout the controlled areas. The positions of detected objects, computed from different sensors, are fused considering a dynamic reliability factor for each sensor reading. Close-up recordings of suspicious events are obtained by tasking the active camera systems (ACSs). The co-operation is performed through a multicast communication system studied to transmit useful data both intra and inter networks. In particular, information about the position of the object to track, sent by a static camera system (SCS), is used by an ACS to operate an initial repositioning. The ACS compensates background changes owing to the camera motion, detects mobile objects in the scene and autonomously tracks the object of interest. Tracking results are presented in the context of a video surveillance application for a parking lot.
Resource-Aware Coverage and Task Assignment in Visual Sensor Networks

A visual sensor network (VSN) consists of a large amount of camera nodes which are able to process the captured image data locally and to extract the relevant information. The tight resource limitations in these networks of embedded sensors and processors represent a major challenge for the application development. In this paper, we focus on finding optimal VSN configurations which are basically given by: 1) the selection of cameras to sufficiently monitor the area of interest; 2) the setting of the cameras' frame rate and resolution to fulfill the quality of service requirements; and 3) the assignment of processing tasks to cameras to achieve all required monitoring activities. We formally specify this configuration problem and describe an efficient approximation method based on an evolutionary algorithm. We analyze our approximation method on three different scenarios and compare the predicted results with measurements on real implementations on a VSN platform. We finally combine our approximation method with an expectation-maximization algorithm for optimizing the coverage and resource allocation in VSN with pan-tilt-zoom camera nodes.
Distributed and Unsupervised Cost-Driven Person Re-Identification

The problem of re-identify persons across single disjoint camera-pairs has received great attention from the community. Despite this, when the re-identification process has to be carried out on a large camera network a different approach has to be considered. In particular, existing approaches have neglected the importance of the network topology (i.e., the structure of the monitored environment) in such a process. To try filling such a gap, we propose a Distributed and Unsupervised Cost-Driven Person Re-Identification framework (DUPRe) which introduces the following contributions: (i) a camera matching cost to measure the re-identification performance between nodes of the network; (ii) a derivation of the distance vector algorithm which allows to learn the network topology hence to prioritize and limit the cameras inquired for the re-identification. Results on two benchmark datasets show that our solution brings to significant network-wise re-identification improvements.
Tuning Asymboost Cascades Improves Face Detection

The face detection problem is certainly one of the most studied topics in artificial vision. This interest raises from the conscience that this is a crucial step for every system that uses biometric information. Video surveillance and security systems, biometrics, HCI and multimedia applications are some examples of systems that exploit face localization to improve their robustness. AdaBoost and AsymBoost based classifiers are widely used to achieve high performances saving computational time. In this paper, a new reactive strategy to build a strong classifier cascade is provided; at each stage of the cascade a different tradeoff between accuracy and computational complexity is explored. The results will show that this method is effective, and propose a way to construct a rapid and robust multipose detector.
Aerial video surveillance system for small-scale UAV environment monitoring

Change detection algorithms are commonly used to detect novelties for surveillance purposes in public and private places equipped by static or Pan-Tilt-Zoom (PTZ) cameras. Often, these techniques are also used as prerequisite to support more complex algorithms, including event recognition, object classification, person re-identification, and many others. With regard to small-scale Unmanned Aerial Vehicles (UAVs) at low-altitude, the change detection techniques require further investigation. In fact, most of the works currently available in the literature process video sequences acquired at very high-altitude for large-scale operations, such as vegetation monitoring, mapping of buildings, and so on. In a wide range of application contexts that require, for example, frequent monitoring or high spatial resolution for detecting small objects, video sequences acquired at high-altitude are not suitable. This paper presents a change detection system based on histogram equalization and RGB-Local Binary Pattern (RGB-LBP) operator for monitoring of wide areas by small-scale UAVs at low-altitude. Extensive experimental results show the robustness of the proposed pipeline. These latter were performed by using challenging video sequences of the public UAV Mosaicking and Change Detection (UMCD) dataset and measured a set of well-known statistical metrics. Finally, a performance analysis of the proposed algorithm is also provided.
A cooperative multicamera system for video-surveillance of parking lots

In this paper, a cooperative system able to manage static and active cameras in a hierarchical framework is presented. The system is able to track multiple targets simultaneously and in real-time through the controlled areas. Objects' positions, computed from different sensors, are fused by taking into account a dynamic reliability factor for each sensor reading.. Close-up recordings of suspicious events can be obtained by tasking an Active-Camera-System (ACS) to operate the available active cameras. Information about the position of the object to track is used by the ACS to operate the initial repositioning. The ACS is able to compensate background changes due to the camera motion and to detect mobile objects in the scene. Tracking results are presented in the context of a video-surveillance application for a parking lot. (5 pages)
The Evolution of Neural Learning Systems: A Novel Architecture Combining the Strengths of NTs, CNNs, and ELMs

Mimicking the human brain to achieve human-level cognition performance has been a core challenge in artificial intelligence research for decades. Humans are very efficient in capturing the most important information while being exposed to a plethora of different stimuli, a capability that is used to represent and understand their surroundings in a concise fashion. Machine learning research has made considerable progress toward cloning such a human capability with innovative techniques like deep learning, feature learning, incremental learning, and so on. In this article, an overview of the mainstream brain-inspired architectures and research directions proposed over the past decade is provided. In addition, a novel architecture exploiting the strengths of the current methods is proposed. Preliminary results demonstrate that it is able to achieve state-of-the-art results in a more efficient way.
Sensor Bandwidth Assignment through Video Annotation

The state of the art of surveillance systems include a large set of techniques for both low level and high level tasks. In particular, the research community has witnessed in the last decade a high proliferation of techniques that span from object detection and tracking to object recogni- tion and event understanding. Although some techniques have been proven to be very effective those tasks cannot be considered solved. Although more effort is needed in the event analysis field, a new problem arises from the develop- ment of large scale networked surveillance systems: infor- mation sharing. The way information is shared between the nodes of the surveillance network today represents a key- point issue. To provide a first and novel solution to such a problem, we propose an innovative system architecture for a video surveillance system with distributed processing over multiple processing units and with distributed communica- tion over multiple heterogeneous channels (wireless, satel- lite, local IP networks, etc.). In particular, a new real-time technique for changing the video transmission parameters (e.g., frame rate, spatial/color resolution, etc.) according to the bandwidth available will be here presented.
Video security for ambient intelligence

Moving toward the implementation of the intelligent building idea in the framework of ambient intelligence, a video security application for people detection, tracking, and counting in indoor environments is presented in this paper. In addition to security purposes, the system may be employed to estimate the number of accesses in public buildings, as well as the preferred followed routes. Computer vision techniques are used to analyze and process video streams acquired from multiple video cameras. Image segmentation is performed to detect moving regions and to calculate the number of people in the scene. Testing was performed on indoor video sequences with different illumination conditions.
Camera Selection for Adaptive Human-Computer Interface

Video analytics has become a very important topic in computer vision. This paper introduces advanced video analytics human-computer interfaces for a video surveillance system to ease the tasks of security operators. The visualization of the most relevant views is provided by the human-computer interface module that preemptively activates cameras that will probably cover the motion of interesting objects. Human-computer interaction principles have been considered to develop the novel user interface. Four prototypes have been designed and usability performance has been evaluated, exploiting standard methods. Results obtained from such evaluations show the efficiency of the novel information visualization technique.
Re-Identification in the Function Space of Feature Warps

Person re-identification in a non-overlapping multicamera scenario is an open challenge in computer vision because of the large changes in appearances caused by variations in viewing angle, lighting, background clutter, and occlusion over multiple cameras. As a result of these variations, features describing the same person get transformed between cameras. To model the transformation of features, the feature space is nonlinearly warped to get the “warp functions”. The warp functions between two instances of the same target form the set of feasible warp functions while those between instances of different targets form the set of infeasible warp functions. In this work, we build upon the observation that feature transformations between cameras lie in a nonlinear function space of all possible feature transformations. The space consisting of all the feasible and infeasible warp functions is the warp function space (WFS). We propose to learn a discriminating surface separating these two sets of warp functions in the WFS and to re-identify persons by classifying a test warp function as feasible or infeasible. Towards this objective, a Random Forest (RF) classifier is employed which effectively chooses the warp function components according to their importance in separating the feasible and the infeasible warp functions in the WFS. Extensive experiments on five datasets are carried out to show the superior performance of the proposed approach over state-of-the-art person re-identification methods. We show that our approach outperforms all other methods when large illumination variations are considered. At the same time it has been shown that our method reaches the best average performance over multiple combinations of the datasets, thus, showing that our method is not designed only to address a specific challenge posed by a particular dataset.
Active Tuning of Intrinsic Camera Parameters

In the last years, the research effort of the scientific community to study systems for ambient intelligence has been really strong. Usually, the systems developed so far base their analysis on images acquired by automatic cameras. In this paper, we propose a way to develop new smart systems that are able to actively decide both what to see and how to see it. In particular, the main idea is to tune the acquisition parameters on the basis of what the system desires to acquire. The regulation strategy is based on two camera parameters, focus and iris. It aims to identify an optimal sequence of steps to enhance the acquisition quality of an object of interest. To this end, a hierarchy of neural networks has been employed first to select which parameter must be regulated then to adjust it. The proposed solution can be applied to both static and moving cameras. The results show how the proposed technique can be applied to images acquired by a moving camera with zoom capabilities for surveillance purposes.