Diseño y Simulación de un Prototipo Experimental Para Agrupar Partículas Usando Caracterización de la Trayectoria

Los implementos de medición en cualquier área de la ciencia infieren un alto costo que muchas veces no son costeables como equipo didáctico dentro de los planes de estudio en la educación de nivel superior, sin embargo, el conocer y saber hacer uso de los implementos de medición competentes al campo de desempeño provee de competencias necesarias a los estudiantes que será reflejado seguramente en su actividad dentro del campo laboral.Es por ello que en este trabajo se diseña y desarrolla un prototipo experimental para el agrupamiento de partículas usando la teoría de caracterización de trayectoria, con la finalidad de utilizarlo como herramienta para reafirmar los conocimientos teóricos yllevar a los estudiantes a un aprendizaje significativo e inducirlos a la investigación científica. Para el procesamiento de los resultados se utilizaron los conceptos de separación gravimétrica de partículas como parte de la materia de Mecánica de Fluidos y herramienta de programación de Matla

Multi-Instance dictionary learning for detecting abnormal events in surveillance videos

In this paper, a novel method termed Multi-Instance Dictionary Learning (MIDL) is presented for detecting abnormal events in crowded video scenes. With respect to multi-instance learning, each event (video clip) in videos is modeled as a bag containing several sub-events (local observations); while each sub-event is regarded as an instance. The MIDL jointly learns a dictionary for sparse representations of sub-events (instances) and multi-instance classifiers for classifying events into normal or abnormal. We further adopt three different multi-instance models, yielding the Max-Pooling-based MIDL (MP-MIDL), Instance-based MIDL (Inst-MIDL) and Bag-based MIDL (Bag-MIDL), for detecting both global and local abnormalities. The MP-MIDL classifies observed events by using bag features extracted via max-pooling over sparse representations. The Inst-MIDL and Bag-MIDL classify observed events by the predicted values of corresponding instances. The proposed MIDL is evaluated and compared with the state-of-the-art methods for abnormal event detection on the UMN (for global abnormalities) and the UCSD (for local abnormalities) datasets and results show that the proposed MP-MIDL and Bag-MIDL achieve either comparable or improved detection performances. The proposed MIDL method is also compared with other multi-instance learning methods on the task and superior results are obtained by the MP-MIDL scheme. </jats:p

A semantic autonomous video surveillance system for dense camera networks in smart cities

Producción CientíficaThis paper presents a proposal of an intelligent video surveillance system able to detect and identify abnormal and alarming situations by analyzing object movement. The system is designed to minimize video processing and transmission, thus allowing a large number of cameras to be deployed on the system, and therefore making it suitable for its usage as an integrated safety and security solution in Smart Cities. Alarm detection is performed on the basis of parameters of the moving objects and their trajectories, and is performed using semantic reasoning and ontologies. This means that the system employs a high-level conceptual language easy to understand for human operators, capable of raising enriched alarms with descriptions of what is happening on the image, and to automate reactions to them such as alerting the appropriate emergency services using the Smart City safety network

KAJIAN SINGKAT TENTANG: PENGENDALIAN DAN PENJEJAKAN OBJEK BERBASIS VISUAL

Pengendalian dan penjejakan objek banyak digunakan di berbagai bidang seperti pendidikan, kesehatan dan kesejahteraan, olahraga, pengawasan pada industri konstuksi, pengawasan di supermarket, dan lain-lain. Sistem pengendalian dan&nbsp; penjejakan objek terdiri dari tiga sub-sistem, yaitu pendeteksi dan pengenalan objek, sistem estimasi pergerakan objek, dan pengendalian perangkat kamera dan aktuatornya agar dapat menjejaki objek. Banyak kajian telah dilakukan tentang beberapa algoritma untuk melakukan deteksi dan pengenalan objek seperti camshift, viola-jones, gaussian mixture model, surf, dan lain-lain. Dengan kelebihan dan kelemahan sebagai berikut (i) ketika data terbatas jumlahnya, (ii) sistem operasi berbasis fitur jauh lebih cepat daripada sistem berbasis piksel, (iii) posisi, ukuran, orientasi objek yang berubah-ubah, dan lain-lain. Algoritma-algortima ini telah dikembangkan untuk estimasi pergerakan objek namun dalam hal ini masih terdapat kelemahaan antara lain pada gerak objek dan lokasi objek yang selalu berubah-ubah dan jumlah objek lebih banyak daripada jumlah kamera. Sedangkan, persoalan yang dihadapi dalam sistem pengendalian adalah ketika penjejakan pada objek terjadi seperti (i) hijacking problem, (ii) centralization problem, (iii) drifting problem. Tujuan dari kajian ini adalah untuk melakukan bahasan dari penelitian mengenai pengendalian dan penjejakan objek, yang telah dilakukan sebelumnya dan untuk mendapatkan titik temu peluang kontribusi dan nilai kebaruan dari bidang ini

An intelligent surveillance platform for large metropolitan areas with dense sensor deployment

Producción CientíficaThis paper presents an intelligent surveillance platform based on the usage of large numbers of inexpensive sensors designed and developed inside the European Eureka Celtic project HuSIMS. With the aim of maximizing the number of deployable units while keeping monetary and resource/bandwidth costs at a minimum, the surveillance platform is based on the usage of inexpensive visual sensors which apply efficient motion detection and tracking algorithms to transform the video signal in a set of motion parameters. In order to automate the analysis of the myriad of data streams generated by the visual sensors, the platform’s control center includes an alarm detection engine which comprises three components applying three different Artificial Intelligence strategies in parallel. These strategies are generic, domain-independent approaches which are able to operate in several domains (traffic surveillance, vandalism prevention, perimeter security, etc.). The architecture is completed with a versatile communication network which facilitates data collection from the visual sensors and alarm and video stream distribution towards the emergency teams. The resulting surveillance system is extremely suitable for its deployment in metropolitan areas, smart cities, and large facilities, mainly because cheap visual sensors and autonomous alarm detection facilitate dense sensor network deployments for wide and detailed coveraMinisterio de Industria, Turismo y Comercio and the Fondo de Desarrollo Regional (FEDER) and the Israeli Chief Scientist Research Grant 43660 inside the European Eureka Celtic project HuSIMS (TSI-020400-2010-102)

Mixed Pattern Matching-Based Traffic Abnormal Behavior Recognition

A motion trajectory is an intuitive representation form in time-space domain for a micromotion behavior of moving target. Trajectory analysis is an important approach to recognize abnormal behaviors of moving targets. Against the complexity of vehicle trajectories, this paper first proposed a trajectory pattern learning method based on dynamic time warping (DTW) and spectral clustering. It introduced the DTW distance to measure the distances between vehicle trajectories and determined the number of clusters automatically by a spectral clustering algorithm based on the distance matrix. Then, it clusters sample data points into different clusters. After the spatial patterns and direction patterns learned from the clusters, a recognition method for detecting vehicle abnormal behaviors based on mixed pattern matching was proposed. The experimental results show that the proposed technical scheme can recognize main types of traffic abnormal behaviors effectively and has good robustness. The real-world application verified its feasibility and the validity

Anomaly detection and classification in traffic flow data from fluctuations in the flow-density relationship

We describe and validate a novel data-driven approach to the real time detection and classification of traffic anomalies based on the identification of atypical fluctuations in the relationship between density and flow. For aggregated data under stationary conditions, flow and density are related by the fundamental diagram. However, high resolution data obtained from modern sensor networks is generally non-stationary and disaggregated. Such data consequently show significant statistical fluctuations. These fluctuations are best described using a bivariate probability distribution in the density-flow plane. By applying kernel density estimation to high-volume data from the UK National Traffic Information Service (NTIS), we empirically construct these distributions for London's M25 motorway. Curves in the density-flow plane are then constructed, analogous to quantiles of univariate distributions. These curves quantitatively separate atypical fluctuations from typical traffic states. Although the algorithm identifies anomalies in general rather than specific events, we find that fluctuations outside the 95\% probability curve correlate strongly with the spikes in travel time associated with significant congestion events. Moreover, the size of an excursion from the typical region provides a simple, real-time measure of the severity of detected anomalies. We validate the algorithm by benchmarking its ability to identify labelled events in historical NTIS data against some commonly used methods from the literature. Detection rate, time-to-detect and false alarm rate are used as metrics and found to be generally comparable except in situations when the speed distribution is bi-modal. In such situations, the new algorithm achieves a much lower false alarm rate without suffering significant degradation on the other metrics. This method has the additional advantage of being self-calibrating.Comment: 23 pages, 12 figure