Crowd monitoring using computer vision

Novel computer vision techniques have been developed for automatic monitoring of crowed environments such as airports, railway stations and shopping malls. Using video feeds from multiple cameras, the techniques enable crowd counting, crowd flow monitoring, queue monitoring and abnormal event detection. The outcome of the research is useful for surveillance applications and for obtaining operational metrics to improve business efficiency.

Detecting rare events using Kullback-Leibler divergence

One main challenge in developing a system for visual surveillance event detection is the annotation of target events in the training data. By making use of the assumption that events with security interest are often rare compared to regular behaviours, this paper presents a novel approach by using Kullback-Leibler (KL) divergence for rare event detection in a weakly supervised learning setting, where only clip-level annotation is available. It will be shown that this approach outperforms state-of-the-art methods on a popular real-world dataset, while preserving real time performance.

Clustering acoustic events in environmental recordings for species richness surveys

Environmental acoustic recordings can be used to perform avian species richness surveys, whereby a trained ornithologist can observe the species present by listening to the recording. This could be made more efficient by using computational methods for iteratively selecting the richest parts of a long recording for the human observer to listen to, a process known as “smart sampling”. This allows scaling up to much larger ecological datasets. In this paper we explore computational approaches based on information and diversity of selected samples. We propose to use an event detection algorithm to estimate the amount of information present in each sample. We further propose to cluster the detected events for a better estimate of this amount of information. Additionally, we present a time dispersal approach to estimating diversity between iteratively selected samples. Combinations of approaches were evaluated on seven 24-hour recordings that have been manually labeled by bird watchers. The results show that on average all the methods we have explored would allow annotators to observe more new species in fewer minutes compared to a baseline of random sampling at dawn.

Application of image processing techniques for frog call classification

Frogs have received increasing attention due to their effectiveness for indicating the environment change. Therefore, it is important to monitor and assess frogs. With the development of sensor techniques, large volumes of audio data (including frog calls) have been collected and need to be analysed. After transforming the audio data into its spectrogram representation using short-time Fourier transform, the visual inspection of this representation motivates us to use image processing techniques for analysing audio data. Applying acoustic event detection (AED) method to spectrograms, acoustic events are firstly detected from which ridges are extracted. Three feature sets, Mel-frequency cepstral coefficients (MFCCs), AED feature set and ridge feature set, are then used for frog call classification with a support vector machine classifier. Fifteen frog species widely spread in Queensland, Australia, are selected to evaluate the proposed method. The experimental results show that ridge feature set can achieve an average classification accuracy of 74.73% which outperforms the MFCCs (38.99%) and AED feature set (67.78%).

Compact features for birdcall retrieval from environmental acoustic recordings

Bioacoustic data can be used for monitoring animal species diversity. The deployment of acoustic sensors enables acoustic monitoring at large temporal and spatial scales. We describe a content-based birdcall retrieval algorithm for the exploration of large data bases of acoustic recordings. In the algorithm, an event-based searching scheme and compact features are developed. In detail, ridge events are detected from audio files using event detection on spectral ridges. Then event alignment is used to search through audio files to locate candidate instances. A similarity measure is then applied to dimension-reduced spectral ridge feature vectors. The event-based searching method processes a smaller list of instances for faster retrieval. The experimental results demonstrate that our features achieve better success rate than existing methods and the feature dimension is greatly reduced.

Detecting rare events using Kullback-Leibler divergence: A weakly supervised approach

Video surveillance infrastructure has been widely installed in public places for security purposes. However, live video feeds are typically monitored by human staff, making the detection of important events as they occur difficult. As such, an expert system that can automatically detect events of interest in surveillance footage is highly desirable. Although a number of approaches have been proposed, they have significant limitations: supervised approaches, which can detect a specific event, ideally require a large number of samples with the event spatially and temporally localised; while unsupervised approaches, which do not require this demanding annotation, can only detect whether an event is abnormal and not specific event types. To overcome these problems, we formulate a weakly-supervised approach using Kullback-Leibler (KL) divergence to detect rare events. The proposed approach leverages the sparse nature of the target events to its advantage, and we show that this data imbalance guarantees the existence of a decision boundary to separate samples that contain the target event from those that do not. This trait, combined with the coarse annotation used by weakly supervised learning (that only indicates approximately when an event occurs), greatly reduces the annotation burden while retaining the ability to detect specific events. Furthermore, the proposed classifier requires only a decision threshold, simplifying its use compared to other weakly supervised approaches. We show that the proposed approach outperforms state-of-the-art methods on a popular real-world traffic surveillance dataset, while preserving real time performance.

Lapset, media ja symbolinen vuorovaikutus : Suomalaisten, englantilaisten ja saksalaisten lasten mediasuhteen tarkastelua

Tämän hetken mediaympäristölle on ominaista intensiivisyys ja jatkuva läsnäolo. Medialla on merkittävä rooli myös pienten lasten jokapäiväisessä elämässä, sillä he aloittavat median säännöllisen seuraamisen keskimäärin kolmen vuoden iässä. Mediasisällöt, mediavälineet ja mediaan liittyvät sosiaaliset suhteet muodostavatkin lapsille mediaympäristön, jossa lapset rakentavat identiteettejään, oppivat sosiaalista kanssakäymistä ja kehittävät näkemyksiään yhteiskunnasta ja kulttuurista. Tutkimuksessa on selvitetty 4-6-vuotiaitten suomalaisten, englantilaisten ja saksalaisten lasten audiovisuaalisen median tulkintaa ja median roolia heidän elämässään. Tutkimuksen tavoitteena on ollut syventää tutkimuksellista tietoa median sosiaalisesta ja kulttuurisesta merkityksestä pienten lasten elämässä ja sitä, miten he tulkitsevat mediasisältöä. Tutkimuksessa lasten mediasuhdetta on tarkasteltu välineellisenä, sosiaalisena, symbolisena ja kulttuurisena tulkintaympäristönä. Edellisten lisäksi tutkimuksessa on arvioitu harvemmin viestinnän tutkimuksessa käytetyn symbolisen interaktionismin teorian tarjoamia mahdollisuuksia lasten mediasuhteen tarkasteluun. Suomessa, Englannissa ja Saksassa kootun kansainvälisen aineiston pohjalta on tarkasteltu myös vertailuryhmien välillä olevia mediaan liittyviä kulttuurisia eroja. Eri vertailumaiden melko samankaltaisesta mediaympäristöstä huolimatta tutkimus antaa viitteitä mediatulkinnoissa olevista kulttuurisista eroista. Media mahdollistaa lapsen erilaistan taitojensa kehittymistä ja voi siten muodostaa heille sosiaalisia, symbolisia ja kulttuurisia resursseja, joilla on merkitystä lapsen kehittymisen kannalta. Lapsen ja median suhde on kaksisuuntainen vuorovaikutussuhde ja mediainformaation tulkinnassa ovat mukana lapsen aiemmat tiedolliset ja sosiaaliset kokemukset. Aktiivisessa mediatulkintasuhteessaan lapsi kehittää sanavarastoaan, havainnointiaan, ajatteluaan ja tunne-elämäänsä. Median käyttö sosiaalisena tapahtumana kehittää osaltaan lapsen sosiaalisia valmiuksia. Siten esimerkiksi perheen median käyttöön liittyvät säännöt ja ohjeet ohjaavat perheen sisäistä toimintaa ja määrittävät lapsen asemaa perheessä. Median sisällöt ja niihin liittyvät erilaiset oheistuotteet toimivat osaltaan lapsen kulttuuristen koodistojen ja luokittelujen muodostajana. Tutkimus osoittaa myös symbolisen interaktionismin teorian tarjoavan varsin poikkitieteellisen tutkimuksellisen viitekehyksen lapsia ja mediaa koskevalle tutkimukselle ja mahdollistaa lasten mediasuhteen tutkimisen ja ymmärtämisen useiden, erilaisten tekijöiden suhteena.

Robust tracking with interest points: A sparse representation approach

Visual tracking is an important task in various computer vision applications including visual surveillance, human computer interaction, event detection, video indexing and retrieval. Recent state of the art sparse representation (SR) based trackers show better robustness than many of the other existing trackers. One of the issues with these SR trackers is low execution speed. The particle filter framework is one of the major aspects responsible for slow execution, and is common to most of the existing SR trackers. In this paper,(1) we propose a robust interest point based tracker in l(1) minimization framework that runs at real-time with performance comparable to the state of the art trackers. In the proposed tracker, the target dictionary is obtained from the patches around target interest points. Next, the interest points from the candidate window of the current frame are obtained. The correspondence between target and candidate points is obtained via solving the proposed l(1) minimization problem. In order to prune the noisy matches, a robust matching criterion is proposed, where only the reliable candidate points that mutually match with target and candidate dictionary elements are considered for tracking. The object is localized by measuring the displacement of these interest points. The reliable candidate patches are used for updating the target dictionary. The performance and accuracy of the proposed tracker is benchmarked with several complex video sequences. The tracker is found to be considerably fast as compared to the reported state of the art trackers. The proposed tracker is further evaluated for various local patch sizes, number of interest points and regularization parameters. The performance of the tracker for various challenges including illumination change, occlusion, and background clutter has been quantified with a benchmark dataset containing 50 videos. (C) 2014 Elsevier B.V. All rights reserved.

Selective data gathering in community sensor networks

Smartphones and other powerful sensor-equipped consumer devices make it possible to sense the physical world at an unprecedented scale. Nearly 2 million Android and iOS devices are activated every day, each carrying numerous sensors and a high-speed internet connection. Whereas traditional sensor networks have typically deployed a fixed number of devices to sense a particular phenomena, community networks can grow as additional participants choose to install apps and join the network. In principle, this allows networks of thousands or millions of sensors to be created quickly and at low cost. However, making reliable inferences about the world using so many community sensors involves several challenges, including scalability, data quality, mobility, and user privacy.

This thesis focuses on how learning at both the sensor- and network-level can provide scalable techniques for data collection and event detection. First, this thesis considers the abstract problem of distributed algorithms for data collection, and proposes a distributed, online approach to selecting which set of sensors should be queried. In addition to providing theoretical guarantees for submodular objective functions, the approach is also compatible with local rules or heuristics for detecting and transmitting potentially valuable observations. Next, the thesis presents a decentralized algorithm for spatial event detection, and describes its use detecting strong earthquakes within the Caltech Community Seismic Network. Despite the fact that strong earthquakes are rare and complex events, and that community sensors can be very noisy, our decentralized anomaly detection approach obtains theoretical guarantees for event detection performance while simultaneously limiting the rate of false alarms.

Community sense and response systems

The proliferation of smartphones and other internet-enabled, sensor-equipped consumer devices enables us to sense and act upon the physical environment in unprecedented ways. This thesis considers Community Sense-and-Response (CSR) systems, a new class of web application for acting on sensory data gathered from participants' personal smart devices. The thesis describes how rare events can be reliably detected using a decentralized anomaly detection architecture that performs client-side anomaly detection and server-side event detection. After analyzing this decentralized anomaly detection approach, the thesis describes how weak but spatially structured events can be detected, despite significant noise, when the events have a sparse representation in an alternative basis. Finally, the thesis describes how the statistical models needed for client-side anomaly detection may be learned efficiently, using limited space, via coresets.

The Caltech Community Seismic Network (CSN) is a prototypical example of a CSR system that harnesses accelerometers in volunteers' smartphones and consumer electronics. Using CSN, this thesis presents the systems and algorithmic techniques to design, build and evaluate a scalable network for real-time awareness of spatial phenomena such as dangerous earthquakes.

Pub/Sub系统中分布式复合事件检测技术研究

随着信息技术的发展，Pub/Sub系统由于具有异步和松耦合的特点，被越来越广泛的应用到金融、供应链管理、物流等领域。在这些应用中，用户对具有各种特定逻辑或时序关系的复合事件的订阅需求越来越迫切，这使得Pub/Sub系统中的分布式复合事件检测技术成为研究的热点，分布式复合事件检测技术包括复合订阅语言、复合匹配算法、以及订阅和事件的路由算法。 目前已有的Pub/Sub系统提供的复合订阅语言比较简单，对时序支持较弱，不能满足实际应用的需要，已有的复合匹配算法也不能有效的支持具有丰富时序关系的复合事件的检测。在路由方面，基于内容的Pub/Sub系统大都是在树结构或者无环图结构的覆盖网络上，采用基于过滤的原子路由方法，该路由方法需要将原子订阅传遍几乎整个网络，以减少订阅匹配的延迟，但这种路由方法很难适应网络的拓扑变化。而目前基于事件空间划分的路由方法不支持事件空间的动态划分和事件空间在不同服务器之间的移动，并且没有提供专门针对事件空间划分的复合事件检测方法。 本文在调研了各种应用需求的基础上，提出了能够表达事件丰富的时序关系、逻辑关系和事件实例关系的复合订阅语言，并且定义了两种事件排序方式。在消费语义采用配对模式的情况下，给出了该语言在两种事件排序方式下的检测结果集的定义。针对该复合订阅语言提出并实现了图结构和时间事件发生器相结合的复合匹配算法，该匹配算法使图结构可以有效的支持时序关系和非触发式事件的检测。在路由方面，首先实现了基于过滤的逆向路径转发的原子路由方法，并在此基础上，实现了就近检测协议，该协议优化了复合订阅匹配结构在网络中的部署。最后，设计和实现了基于事件空间划分的原子路由方法，该路由方法实现了事件空间的动态划分，并可以根据系统中服务器的负载情况实现事件空间的移动，从而有效的平衡服务器的负载。在此基础上，通过对复合订阅的拆分，并利用可移动的复合事件检测器实现了复合订阅的分布式部署和复合事件的分布式检测，同时通过对复合事件检测器的复用，进一步减少了网络负载和服务器的匹配负载。本文还通过实验验证了匹配算法和两种路由方法的性能和开销。

Społeczna interpretacja wybranych wczesnośredniowiecznych cmentarzysk rzędowych w perspektywie gender studies i lifecycle analysis

Wydział Historyczny: Instytut Prahistorii

La Web Semántica y los archivos de televisión: estado de la cuestión

Se analizan y describen las principales líneas de trabajo de la Web Semántica en el ámbito de los archivos de televisión. Para ello, se analiza y contextualiza la web semántica desde una perspectiva general para posteriormente analizar las principales iniciativas que trabajan con lo audiovisual: Proyecto MuNCH, Proyecto S5T, Semantic Television y VideoActive.