José M. Martínez

Comparative Evaluation of Stationary Foreground Object Detection Algorithms Based on Background Subtraction Techniques

In several video surveillance applications, such as the detection of abandoned/stolen objects or parked vehicles,the detection of stationary foreground objects is a critical task. In the literature, many algorithms have been proposed that deal with the detection of stationary foreground objects, the majority of them based on background subtraction techniques. In this paper we discuss various stationary object detection approaches comparing them in typical surveillance scenarios (extracted from standard datasets). Firstly, the existing approaches based on background-subtraction are organized into categories. Then, a representative technique of each category is selected and described. Finally, a comparative evaluation using objective and subjective criteria is performed on video surveillance sequences selected from the PETS 2006 and i-LIDS forAVSS 2007 datasets, analyzing the advantages and drawbacks of each selected approach.

On the mobility of polycrystalline semiconductors

Abstract A simple phenomenological model based on tunnel and thermionic emission across grain boundary barrier has been developed. The present model has been applied to polycrystalline silicon assuming that the dangling bonds at the grain boundaries behave as electron traps. The calculations have been carried out in two different ways; one, assuming that the interface states density, N is (cm −2 eV −1 ), is constant across the energy gap; and second with the boundary states, N T (cm −2 ), localized around a very narrow energy range at E T . In the first case no differences in the mobility reduction have been found between n and p type polysilicon, but for the assumption of states localized at an energy E T in the upper half of the gap , the barrier height is larger in n -type than in p -type material and consequently the calculated mobility of n -type polysilicon becomes lower than the p -type mobility. In general, the mobility increases with the dopant concentration approaching the monocrystalline behaviour for very large dopings, in qualitative agreement with other approaches and with available experiments.

Robust Unattended and Stolen Object Detection by Fusing Simple Algorithms

In this paper a new approach for detecting unattended or stolen objects in surveillance video is proposed. It is based on the fusion of evidence provided by three simple detectors. As a first step, the moving regions in the scene are detected and tracked. Then, these regions are classified as static or dynamic objects and human or nonhuman objects. Finally, objects detected as static and nonhuman are analyzed with each detector. Data from these detectors are fused together to select the best detection hypotheses. Experimental results show that the fusion-based approach increases the detection reliability as compared to the detectors and performs considerably well across a variety of multiple scenarios operating at realtime.

An Ontology for Event Detection and its Application in Surveillance Video

In this paper, we propose an ontology for representing the prior knowledge related to video event analysis. It is composed of two types of knowledge related to the application domain and the analysis system. Domain knowledge involves all the high level semantic concepts in the context of each examined domain (objects, events, context...) whilst system knowledge involves the capabilities of the analysis system (algorithms, reactions to events...). The proposed ontology has been structured in two parts: the basic ontology (composed of the basic concepts and their specializations) and the domain-specific extensions. Additionally, a video analysis framework based on the proposed ontology is defined for the analysis of different application domains showing the potential use of the proposed ontology. In order to show the real applicability of the proposed ontology, it is specialized for the Underground video-surveillance domain showing some results that demonstrate the usability and effectiveness of the proposed ontology.

Stationary foreground detection using background subtraction and temporal difference in video surveillance

In this paper we describe a new algorithm focused on obtaining stationary foreground regions, which is useful for applications like the detection of abandoned/stolen objects and parked vehicles. Firstly, a sub-sampling scheme based on background subtraction techniques is implemented to obtain stationary foreground regions. Secondly, some modifications are introduced on this base algorithm with the purpose of reducing the amount of stationary foreground detected. Finally, we evaluate the proposed algorithm and compare results with the base algorithm using video surveillance sequences from PETS 2006, PETS 2007 and I-LIDS for AVSS 2007 datasets. Experimental results show that the proposed algorithm increases the detection of stationary foreground regions as compared to the base algorithm.

Grain boundary potential determination in polycrystalline silicon by the scanning light spot technique

Local grain boundary potential has been determined by scanning light spot technique. Photocurrent peaks vary from boundary to boundary giving a strong peak for large‐angle grain boundaries and becoming negligible for the smallest‐angle grain boundaries observed. The largest potentials had typical values of around 0.3 eV and the corresponding interface state densities were in the 1013 cm−2 range.

Adaptive Online Performance Evaluation of Video Trackers

We propose an adaptive framework to estimate the quality of video tracking algorithms without ground-truth data. The framework is divided into two main stages, namely, the estimation of the tracker condition to identify temporal segments during which a target is lost and the measurement of the quality of the estimated track when the tracker is successful. A key novelty of the proposed framework is the capability of evaluating video trackers with multiple failures and recoveries over long sequences. Successful tracking is identified by analyzing the uncertainty of the tracker, whereas track recovery from errors is determined based on the time-reversibility constraint. The proposed approach is demonstrated on a particle filter tracker over a heterogeneous data set. Experimental results show the effectiveness and robustness of the proposed framework that improves state-of-the-art approaches in the presence of tracking challenges such as occlusions, illumination changes, and clutter and on sequences containing multiple tracking errors and recoveries.

Real-time user independent hand gesture recognition from time-of-flight camera video using static and dynamic models

The use of hand gestures offers an alternative to the commonly used human computer interfaces, providing a more intuitive way of navigating among menus and multimedia applications. This paper presents a system for hand gesture recognition devoted to control windows applications. Starting from the images captured by a time-of-flight camera (a camera that produces images with an intensity level inversely proportional to the depth of the objects observed) the system performs hand segmentation as well as a low-level extraction of potentially relevant features which are related to the morphological representation of the hand silhouette. Classification based on these features discriminates between a set of possible static hand postures which results, combined with the estimated motion pattern of the hand, in the recognition of dynamic hand gestures. The whole system works in real-time, allowing practical interaction between user and application.

MPEG-7: Overview of MPEG-7 Description Tools, Part 2

ion nonperceivable Interpretation perceivable

Event Detection and Clustering for Surveillance Video Summarization

The target of surveillance summarization is to identify high-value information events in a video stream and to present it to a user. In this paper we present surveillance summarization approach using detection and clustering of important events. Assuming that events are main source of energy change between consecutive frames set of interesting frames is extracted and then clustered. Based on the structure of clusters two types of summaries are created static and dynamic. Static summary is build of key frames that are organized in clusters. Dynamic summary is created from short video segments representing each cluster and is used to lead user to the event of interest captures in key frames. We describe our approach and present experimental results.