Traditional object detection methods struggle in complex logistics and warehousing environments due to their inability to effectively identify regions of interest while handling extreme occlusion, high object density, and significant scale variations with static. Existing region proposal approaches (anchor-based, anchor-free, and deep learning-based) require meticulous hyperparameter tuning, struggle with small or overlapping objects, have difficulties with different lighting conditions or suffer from poor localization in cluttered scenes. We introduce Adaptive Tiles , a novel preprocessing algorithm that dynamically identifies potential object regions prior to detection, thereby addressing these limitations. The core mechanism involves a three-step process: (1) zero-shot segmentation for adaptable, object-agnostic mask generation, (2) filter heuristics to refine masks into robust region proposals, and (3) bounding box combination for merging proposals into optimal detection zones. By shifting from static region identification methods to a dynamic, segmentation-driven proposal mechanism, Adaptive Tiles significantly enhances the efficiency and accuracy of object detectors, as demonstrated by its integration into the DMZoomNet framework and evaluation on the challenging LOCO dataset.

This paper introduces a new system for mapping the coverage provided by antennas using autonomous drones and data fusion techniques. The primary objective of the system is to leverage a contextual layer that provides essential information regarding mobile antennas and their characteristics, such as type, operating frequency, and geodetic coordinates, among other relevant parameters. Based on this contextual layer, the system infers an initial coverage map, which relies entirely on analytical models to estimate free-space power loss as a function of signal propagation distance and frequency. This preliminary estimate is subsequently refined and adjusted to generate a coverage map that more accurately reflects real-world conditions. The refinement process is carried out through measurements acquired at various spatial locations by the drone, which undergo a spatial regression process that employs fully connected neural networks. The study demonstrates how small-scale autonomous drone missions can efficiently sample regions within the target mapping area. These sampled data points enable, through the fusion and filtering process, the spatial propagation of power loss corrections, thereby enhancing the contextual layer and yielding a significantly more precise coverage map.

This paper shows an innovative approach to road monitoring by integrating autonomous drones and sensor fusion within a computer vision-based system. By employing different sets of algorithms, drones equipped with cameras, GPS, and ultrasonic distance sensors can efficiently detect and geolocate road damages, providing crucial data for maintenance and infrastructure management. The system’s key components include automated planning, autonomous flight capabilities, object detection, and sensor fusion techniques, enhancing scalability and adaptability. The main objective is to use context information to compute a flying plan where the subsequent detection of defects allows us to expand and enhance GIS data. The implementation of such a system holds significant potential for improving road safety and optimizing maintenance costs, marking a notable advancement in the convergence of autonomous technologies, sensor fusion, and computer vision for effective road surveillance.

In the field of the intralogistics industry, we present DMZoomNet, a novel architecture that combines deep learning-based detectors with distance information to enhance object detection performance. Evaluation of our approach is conducted using the LOCO dataset, one of the few open source datasets available specifically designed for intralogistics scenarios. By comparing DMZoomNet with existing detectors and object detection methods, we demonstrate its superiority in several object detection metrics within complex intralogistics environments, such as warehouses densely packed with objects. This work contributes to the advancement of object detection techniques in the intralogistics industry and paves the way for future research and applications in this domain.

Within the EUROCONTROL Air Traffic Management (ATM) architecture, the Surveillance Analysis Support System for ATC Centres (SASS-C) is dedicated to the validation of Air Navigation Service Providers. One of its applications is the opportunity Traffic Reconstruction (OTR), which seeks to reconstruct all trajectories by combining noisy measurements from different sensors. It relies on association, tracking and fusion algorithms to determine the true motion of the aircraft on both the vertical and horizontal axes, alongside contextual information useful for identifying the aircraft’s flight mode at any given time. This paper focuses on the filtering of the vertical dimension and outlines certain problems present in the system: the effect of quantized measurements, the appearance of mode of flight transition overshoots and the low reactivity of the filter to abrupt transitions. These problems are analyzed and preliminary improvements according to the ATC context are implemented to overcome them. To demonstrate the improvement, a comparison between the proposed system and the original one is carried out through synthetic trajectory analysis.

UAVs are a cutting-edge technology whose use is currently highly restricted due to their potentially dangerous characteristics, and due to the lack of legislation adopting them and allowing a safe control of these vehicles. Unmanned Air System Traffic Management (UTM) initiatives seek to regularise their use by means of validation and monitoring techniques for the trajectories of these aircraft, both before flight and in real time. For this purpose, in the UTM framework, drones will be collaborative using similar systems to AIS and ADSB for ships or aircraft vehicles. Currently there are no UAV trajectory datasets that allow research in this field, so in this paper a dataset composed of the position and kinematics of the drones over time has been designed. By means of this dataset it is possible to develop and evaluate machine learning methods that help the verification entity to fulfil its functionalities. In this work we propose a first approach to extract useful information by classifying the type of drone based on its movement dynamics. This information would be useful in the identification of the validity of the proposed trajectory for the airframe indicated by the user. The code used for this research is available at https://github.com/DavidSanpedrochez/UAVTrackClassification

The role of the military has been continuously evolving and expanding beyond traditional warfare. In addition to an increasingly asynchronous battlefield, today's military is involved in supporting peacekeeping, crisis management, and disaster relief, to name just a few. Such activities require intense contact with diverse non-military organizations such as government, police, emergency services, relief agencies, and religious leaders. As a result, the information needed for commanders and other decision-makers must come from a wide variety of sources in a wide variety of formats. Thus, any attempt at fusion of this information requires an underlying system concept that is able to standardize information from a variety of input sources, including device-derived information (sensors) and (multiple) natural language(s), as well as to understand complex context, both static and dynamic, and to be able to deal with measures of uncertainty which may vary from source-type to source-type. This paper presents several lessons learned from the ongoing work of a series of NATO Research Task Groups (which have focused on the changing data and information incorporating structured and unstructured human generated information with device-generated data needs for fusion in increasingly complex scenarios requiring interaction between devices, human-generated information and complex contextual backgrounds. We discuss the differing challenges facing the synergic information processing as scenario complexity grows.

The paper presents a group behavior representation and an application in the 2 vs. 2 basketball domain. Furthermore a set of forty features have been made from the raw information provided by the INEF12 Basketball Dataset. Moreover, from all these features we propose a selection using an algorithm to choose the best features to classify and predict the group behavior. The entire experimental test carried out with Hidden Markov Models algorithms could validate the proposed representation and features selection, in group behavior recognition and 2 vs. 2 basketball specific domain.

The multi-objective neural EDA (MONEDA) was proposed with the aim of overcoming some difficulties of current MOEDAs. MONEDA has been shown to yield relevant results when confronted with complex problems. Furthermore, its performance has been shown to adequately adapt to problems with many objectives. Nevertheless, one key issue remains to be studied: MONEDA scalability with regard to the number of decision variables. In this paper has a two-fold purpose. On one hand we propose a modification of MONEDA that incorporates an indicator-based selection mechanism based on the HypE algorithm, while, on the other, we assess the indicator-based MONEDA when solving some complex two-objective problems, in particular problems UF1 to UF7 of the CEC 2009 MOP competition, configured with a progressively-increasing number of decision variables.

Representation and reasoning with spatial properties is essential in several application domains where ontologies are being successfully applied; e.g., Information Fusion systems. This requires a full characterization of the semantics of relations such as adjacent, included, overlapping, etc. Nevertheless, ontologies are not expressive enough to directly support widely-use spatial or topological theories, such as the Region Connection Calculus (RCC). In addition, these properties must be properly instantiated in the ontology, which may require expensive calculations. This paper presents a practical approach to represent and reason with topological properties in ontology-based systems, as well as some optimization techniques that have been applied in a video-based Information Fusion application.

Abstract: Current Computer Vision systems are expected to allow for the management of data acquired by physically distributed cameras. This is especially the case for modern surveillance systems, which require communication between components and a combination of their outputs in order to obtain a complete view of the scene. Information fusion techniques have been successfully applied in this area, but several problems remain unsolved. One of them is the increasing need for coordination and cooperation between independent and heterogeneous cameras. A solution to achieve an understanding between them is to use a common and well‐defined message content vocabulary. In this research work, we present a formal ontology aimed at the symbolic representation of visual data, mainly detected tracks corresponding to real‐world moving objects. Such an ontological representation provides support for spontaneous communication and component interoperability, increases system scalability and facilitates the development of high‐level fusion procedures. The ontology is used by the agents of Cooperative Surveillance Multi‐Agent System, our multi‐agent framework for multi‐camera surveillance systems.

Soft computing methods, and Multi-Objective Evolutionary Algorithms (MOEAs) in particular, lack a general convergence criterion which prevents these algorithms from detecting the generation where further evolution will provide little improvements (or none at all) over the current solution, making them waste computational resources. This paper presents the Least Squares Stopping Criterion (LSSC), an easily configurable and implementable, robust and efficient stopping criterion, based on simple statistical parameters and residue analysis, which tries to introduce as few setup parameters as possible, being them always related to the MOEAs research field rather than the techniques applied by the criterion.

In this work we present a novel progress indicator, called fitness homogeneity indicator (FHI). This indicator improves the other previously discussed indicators as it takes into account all possible processes taking place in the population while not requiring an intensive computation as it relies on the fitness values calculated for the individuals. It is also capable of equally detecting success and failure scenarios, hopefully making an early detection of the second case.

In this work we analyze the model-building issue and the requirements it imposes on the learning paradigm being used. We argue that error-based learning, the class of learning most commonly used in MOEDAs, is responsible for current MOEDA underachievement. We present ART as a viable alternative and present a novel algorithm called multi-objective ART-based EDA (MARTEDA) that uses a Gaussian ART neural network for model-building and an hypervolume based selector as described for the HypE algorithm. We experimentally show that thanks to MARTEDA's novel model-building approach and an indicator-based population ranking the algorithm it is able to outperform similar MOEDAs and MOEAs.

Classical tracking methods are often insufficient when dealing with complex scenarios. In order to solve tracking errors, innovative techniques based on the use of information about the context of the scene have been proposed. Context information ranges from precise measures computed on the pixels of the object neighborhood to high level representations of the entities and the activities of the scene. In this work, we focus on the second approach and propose an ontology-based extension of a general tracking procedure that reasons with abstract context descriptions to improve its accuracy. We describe the design of this extension and how reasoning is performed, as well as its advantages in surveillance scenarios.

In this work we put forward a comprehensive study on the design of global stopping criteria for multi-objective optimization. We describe a novel stopping criterion, denominated MGBM criterion that combines the mutual domination rate (MDR) improvement indicator with a simplified Kalman filter that is used for evidence gathering process. The MDR indicator, which is introduced along, is a special purpose solution meant for the stopping task. It is capable of gauging the progress of the optimization with a low computational cost and therefore suitable for solving complex or many-objective problems. The viability of the proposal is established by comparing it with some other possible alternatives. It should be noted that, although the criteria discussed here are meant for MOPs and MOEAs, they could be easily adapted to other softcomputing or numerical methods by substituting the local improvement metric with a suitable one.

The need for a stopping criterion in MOEA's is a repeatedly mentioned matter in the domain of MOOP's, even though it is usually left aside as secondary, while stopping criteria are still usually based on an a-priori chosen number of maximum iterations. In this paper we want to present a stopping criterion for MOEA's based on three different indicators already present in the community. These indicators, some of which were originally designed for solution quality measuring (as a function of the distance to the optimal Pareto front), will be processed so they can be applied as part of a global criterion, based on estimation theory to achieve a cumulative evidence measure to be used in the stopping decision (by means of a Kalman filter). The implications of this cumulative evidence are analyzed, to get a problem and algorithm independent stopping criterion (for each individual indicator). Finally, the stopping criterion is presented from a data fusion perspective, using the different individual indicators' stopping criteria together, in order to get a final global stopping criterion.

The multi-objective optimization neural estimation of distribution algorithm (MONEDA) was devised with the purpose of dealing with the model-building issues of MOEDAs and, therefore address their scalability.

This paper addresses a real-world engineering design requiring the application of effective and global optimization techniques. The problem it deals with is the design of nonlinear tracking filters under up to several hundreds of performance specifications. The suitability of different evolutionary computation techniques for solving multiobjective problems is explored, contrasting the performance achieved with recent multiobjective evolutionary algorithm (MOEAs) proposals and different aggregation schemes. In particular, a new scheme is proposed to build a fitness function based on an operator that selects worst cases of multiple specifications in different situations. They have been evaluated in the design of an air traffic control (ATC) tracking filter that should accomplish a specific normative with 264 specifications. Results show their performance in terms of effectiveness and computational load, comparing their capability to scale the problem with respect to problem size.

In order to comprehend the advantages and short-comings of each model-building algorithm they should be tested under similar conditions and isolated from the MOEDA it takes part of. In this work we will assess some of the main machine learning algorithms used or suitable for model-building in a controlled environment and under equal conditions. They are analyzed in terms of solution accuracy and computational complexity. To the best of our knowledge a study like this has not been put forward before and it is essential for the understanding of the nature of the model-building problem of MOEDAs and how they should be improved to achieve a quantum leap in their problem solving capacity.

In this work the data association problem in visual tracking is formulated as a combinatorial hypotheses search with a heuristic evaluation function taking into account structural and specific information such as distance, shape, colour, etc. In order to guarantee real time performance, the search process has a time limit to explore alternative solutions. This time defines the upper bound of the number of evaluations depending on the efficiency of the search algorithm. Estimation distribution algorithms (EDA) is proposed as an efficient evolutionary computation technique to search in this hypothesis space. Then, an exhaustive comparison of the performance of alternative algorithms is carried out considering complex representative situations in real video sets.

In this paper we explore the model-building issue of multiobjective optimization estimation of distribution algorithms. We argue that model-building has some characteristics that differentiate it from other machine learning tasks. A novel algorithm called multiobjective neural estimation of distribution algorithm (MONEDA) is proposed to meet those characteristics. This algorithm uses a custom version of the growing neural gas (GNG) network specially meant for the model-building task. As part of this work, MONEDA is assessed with regard to other classical and state-of-the-art evolutionary multiobjective optimizers when solving some community accepted test problems.

This paper investigates combinatorial and probabilistic approaches to real-time video target tracking. Of special interest are real-world scenarios, in which the presence of multiple targets and complex background pose a non-trivial challenge to automated trackers. Object tracking in an exemplary surveillance video sequence is accomplished by means of selected visual tracking techniques, based on two families of methods, combinatorial data association and Particle Filters. Based on the detailed analysis of the performance of the trackers tested, the advantages, complementary failure modes and computational requirements of each method have been identified. Taking into account the results obtained, the hybrid strategy for improved tracking performance is suggested, bringing together the best complementary features of the different tracking methods.

In this paper an approach for multi-sensor coordination in a multiagent visual sensor network is presented. A belief-desire-intention model of multiagent systems is employed. In this multiagent system, the interactions between several surveillance-sensor agents and their respective fusion agent are discussed. The surveillance process is improved using a bottom-up/top-down coordination approach, in which a fusion agent controls the coordination process. In the bottom-up phase the information is sent to the fusion agent. On the other hand, in the top-down stage, feedback messages are sent to those surveillance-sensor agents that are performing an inconsistency tracking process with regard to the global fused tracking process. This feedback information allows to the surveillance-sensor agent to correct its tracking process. Finally, preliminary experiments with the PETS 2006 database are presented.

This paper describes two tracking filters based on the use of kinematic information (velocity, acceleration), in addition to usual position measurements. This kinematic information allows for more advanced filtering methods, reducing error especially on maneuvers. In the paper we will show two different Kalman filter exploiting this information, and compare them with regards to accuracy, computational load ... Mode S Enhanced Surveillance measures will be used as an example for the application of those filters.

A surveillance system that fuses data from several data sources is more robust than those which depends on a single source of input. Fusing the information acquired by a vision system is a difficult task since the system needs to use reliable models for errors and take into account bad performance when taking measurements. In this research, we use a bidimensional object correspondence and tracking method based on the ground plane projection of the blob centroid. We propose a robust method that employs a two phase algorithm which uses a heuristic value and context information to automatically combine each source of information. The fusion process is carried out by a fusion agent in a multi-agent surveillance system. The experimental results on real video sequences have showed the effectiveness and robustness of the system.

This work addresses off-line accurate trajectory reconstruction for air traffic control. We propose the use of specific dynamic models after identification of regular motion patterns. Datasets recorded from opportunity traffic are first segmented in motion segments, based on the mode probabilities of an IMM filter. Then, reconstruction is applied with an optimal smoothing filter operating forward and backward. The parameters describing the specific modes are estimated and then used as external input for smoothing filters. The performance of this approach is compared with a method based on interpolation B-splines. Comparative results on simulated and real data are discussed at the end.

Video target tracking is a complex task, specially when the tracking system is expected to work well in different scenarios. For this reason, this paper proposes an architecture based on a two layer image-processing modules: general tracking layer (GTL) and context layer (CL). GTL describes a generic multipurpose tracking process for video surveillance systems. CL is designed as a symbolic reasoning system that manages the symbolic interface data between GTL modules in order to assess a specific situation and take the appropriate decision. CL intervenes at three different stages of the tracking process, these are initialization, association and update. Our architecture has been tested in two different scenarios to show the advantages in improved performance and output continuity.

In this work we present a novel and efficient algorithm independent stopping criterion, called the MGBM criterion,suitable for Multi-objective Optimization Evolutionary Algorithms(MOEAs).The criterion, after each iteration of the optimization algorithm, gathers evidence of the improvement of the solutions obtained so far. A global (execution wise) evidence accumulation process inspired by recursive Bayesian estimation decides when the optimization should be stopped. Evidence is collected using a novel relative improvement measure constructed on top of the Pareto dominance relations. The evidence gathered after each iteration is accumulated and updated following a rule based on a simplified version of a discrete Kalman filter.Our criterion is particularly useful in complex and/or high-dimensional problems where the traditional procedure of stopping after a predefined amount of iterations cannot beused and the waste of computational resources can induceto a detriment of the quality of the results.Although the criterion discussed here is meant for MOEAs,it can be easily adapted to other soft computing or numerical methods by substituting the local improvement metric witha suitable one.

The growing interest in surveillance in public, military and commercial scenarios is increasing the need to create intelligent or automated distributed visual surveillance systems. Many applications based on distributed resources use the software agent paradigm. In this work, a multi-agent framework is applied to coordinate an indoor-surveillance system based on video cameras. The capacity of coordination will allow the improvement of the global image and the effectiveness of the task distribution. Software agents are embedded in each camera and control the capture parameters. The multi-agent framework allows the coordination of the acquisition procedure based on high level messages and the fusion of information among agents. The agent paradigm uses the internal interpretation of the situation from each agent to improve the global coordination

This work addresses the application of a machine-learning approach to classify ATC trajectory segments from recorded opportunity traffic. It is based on the mode probabilities estimated by an IMM tracking filter operating forward and backward over available data. A learning algorithm creates a rule base for classification from these data, once they have been properly prepared. Performance of this data-driven classification system is compared with a more conventional approach based on transition detection on simulated and real data of representative situations. The offline processing of real data allows an accurate classification of manoeuvring segments, with the possibility of synthesizing ground truth lines for performance evaluation

The evolution of airborne communication systems and ground stations make it possible to improve the performance of tracking systems, in order to increase capacity, safety and efficiency of air traffic environments. These new systems enable downlinking several aircraft parameters for use in ground air traffic management (ATM) systems. Some of these parameters can be used for ATM system function enhancements, such as aircraft tracking. Thus, in this paper, a robust architecture is proposed using downlink airborne parameters (DAPs). This architecture consists of two filters in parallel: one is in charge of dealing with DAPs and the other one cope with the absence of them. Results showing the improvements in performance of the whole system are presented, as well as results coming from the DAP based filter alone. Both results will be compared with the ones delivered by the conventional filter

The use of distributed artificial intelligence (DAI) techniques, particularly the multiagent systems theory, in a decentralized architecture, is proposed to manage cooperatively, all sensor tasks in a network of (air) surveillance radars with capabilities for autonomous operation. At the multisensor data fusion (DF) center, the fusion agent will periodically deliver to sensor agents a list with the system-level tasks that need to be fulfilled. For each system task, indications about its system-level priority are included (inferred global necessity of fulfilling the task) as well as the performance objectives that are required, expressed in different terms depending on the type of task (sector surveillance, target tracking, target identification, etc.). Periodically, the local manager at each sensor (the sensor agent) will decide on the list of sensor-level tasks to be executed by its sensor, providing also the sensor-level priority and performance objectives for each task. The problem of sensor(s)-to-task(s) assignment (including decomposition of system-level tasks into sensor-level tasks and translation of system-level performance requirements to sensor-level performance objectives) is the result of a negotiation process performed among sensor agents, initiated with the information sent to them by the fusion agent. With types of agents, a symbolic bottom-up fuzzy reasoning process is performed that considers the available fused or local target tracks, surveillance sectors data, and (external) intelligence information. As a result of these reasoning processes, performed at each agent planning level, the priorities of system-level and sensor-level tasks will be inferred and applied during the negation process. © 2003 Wiley Periodicals, Inc.

This paper describes the optimisation of some parameters of an optical character recognition system (OCR). The optimisation is performed by means of evolution strategies (ES) in order to maximize the pattern discrimination. The pattern set is a vectorial representation of the character set. The OCR is applied to identify the tail number of an aircraft moving on the airfield runway. The proposed approach is discussed together with some results obtained on a benchmark data set of aircraft tail numbers.

A new approach for data association problems in video image sequences is presented, which uses JPDA formulation adapted to cope with video data peculiarities. A correlation level is computed to weight each blob contribution to each track, by means of a fuzzy system integrating different heuristics inferred from system performance under real situations. Results obtained in representative ground operations show the system capabilities to solve complex scenarios and improve tracking accuracy.

We present a design procedure, based on ES optimisation, to parametrize IMM tracking structures for air traffic control applications. The objective, not addressed in the available bibliography on IMM filters, is to find the most suitable parameters for a selected IMM structure, accordingly to a determined a set of performance specifications. Two alternative structures are analysed following this procedure, showing the best performance achievable by each one, in terms of the proximity of the performance metrics to the specified values.