Detecting complex events in user-generated video using concept classifiers

Automatic detection of complex events in user-generated videos (UGV) is a challenging task due to its new characteristics differing from broadcast video. In this work, we firstly summarize the new characteristics of UGV, and then explore how to utilize concept classifiers to recognize complex events in UGV content. The method starts from manually selecting a variety of relevant concepts, followed byconstructing classifiers for these concepts. Finally, complex event detectors are learned by using the concatenated probabilistic scores of these concept classifiers as features. Further, we also compare three different fusion operations of probabilistic scores, namely Maximum, Average and Minimum fusion. Experimental results suggest that our method provides promising results. It also shows that Maximum fusion tends to give better performance for most complex events

Detecting semantic concepts in digital photographs: low-level features vs. non-homogeneous data fusion

Semantic concepts, such as faces, buildings, and other real world objects, are the most preferred instrument that humans use to navigate through and retrieve visual content from large multimedia databases. Semantic annotation of visual content in large collections is therefore essential if ease of access and use is to be ensured. Classification of images into broad categories such as indoor/outdoor, building/non-building, urban/landscape, people/no-people, etc., allows us to obtain the semantic labels without the full knowledge of all objects in the scene. Inferring the presence of high-level semantic concepts from low-level visual features is a research topic that has been attracting a significant amount of interest lately. However, the power of lowlevel visual features alone has been shown to be limited when faced with the task of semantic scene classification in heterogeneous, unconstrained, broad-topic image collections. Multi-modal fusion or combination of information from different modalities has been identified as one possible way of overcoming the limitations of single-mode approaches. In the field of digital photography, the incorporation of readily available camera metadata, i.e. information about the image capture conditions stored in the EXIF header of each image, along with the GPS information, offers a way to move towards a better understanding of the imaged scene. In this thesis we focus on detection of semantic concepts such as artificial text in video and large buildings in digital photographs, and examine how fusion of low-level visual features with selected camera metadata, using a Support Vector Machine as an integration device, affects the performance of the building detector in a genuine personal photo collection. We implemented two approaches to detection of buildings that combine content-based and the context-based information, and an approach to indoor/outdoor classification based exclusively on camera metadata. An outdoor detection rate of 85.6% was obtained using camera metadata only. The first approach to building detection, based on simple edge orientation-based features extracted at three different scales, has been tested on a dataset of 1720 outdoor images, with a classification accuracy of 88.22%. The second approach integrates the edge orientation-based features with the camera metadata-based features, both at the feature and at the decision level. The fusion approaches have been evaluated using an unconstrained dataset of 8000 genuine consumer photographs. The experiments demonstrate that the fusion approaches outperform the visual features-only approach by of 2-3% on average regardless of the operating point chosen, while all the performance measures are approximately 4% below the upper limit of performance. The early fusion approach consistently improves all performance measures

Fast human behavior analysis for scene understanding

Human behavior analysis has become an active topic of great interest and relevance for a number of applications and areas of research. The research in recent years has been considerably driven by the growing level of criminal behavior in large urban areas and increase of terroristic actions. Also, accurate behavior studies have been applied to sports analysis systems and are emerging in healthcare. When compared to conventional action recognition used in security applications, human behavior analysis techniques designed for embedded applications should satisfy the following technical requirements: (1) Behavior analysis should provide scalable and robust results; (2) High-processing efficiency to achieve (near) real-time operation with low-cost hardware; (3) Extensibility for multiple-camera setup including 3-D modeling to facilitate human behavior understanding and description in various events. The key to our problem statement is that we intend to improve behavior analysis performance while preserving the efficiency of the designed techniques, to allow implementation in embedded environments. More specifically, we look into (1) fast multi-level algorithms incorporating specific domain knowledge, and (2) 3-D configuration techniques for overall enhanced performance. If possible, we explore the performance of the current behavior-analysis techniques for improving accuracy and scalability. To fulfill the above technical requirements and tackle the research problems, we propose a flexible behavior-analysis framework consisting of three processing-layers: (1) pixel-based processing (background modeling with pixel labeling), (2) object-based modeling (human detection, tracking and posture analysis), and (3) event-based analysis (semantic event understanding). In Chapter 3, we specifically contribute to the analysis of individual human behavior. A novel body representation is proposed for posture classification based on a silhouette feature. Only pure binary-shape information is used for posture classification without texture/color or any explicit body models. To this end, we have studied an efficient HV-PCA shape-based descriptor with temporal modeling, which achieves a posture-recognition accuracy rate of about 86% and outperforms other existing proposals. As our human motion scheme is efficient and achieves a fast performance (6-8 frames/second), it enables a fast surveillance system or further analysis of human behavior. In addition, a body-part detection approach is presented. The color and body ratio are combined to provide clues for human body detection and classification. The conventional assumption of up-right body posture is not required. Afterwards, we design and construct a specific framework for fast algorithms and apply them in two applications: tennis sports analysis and surveillance. Chapter 4 deals with tennis sports analysis and presents an automatic real-time system for multi-level analysis of tennis video sequences. First, we employ a 3-D camera model to bridge the pixel-level, object-level and scene-level of tennis sports analysis. Second, a weighted linear model combining the visual cues in the real-world domain is proposed to identify various events. The experimentally found event extraction rate of the system is about 90%. Also, audio signals are combined to enhance the scene analysis performance. The complete proposed application is efficient enough to obtain a real-time or near real-time performance (2-3 frames/second for 720×576 resolution, and 5-7 frames/second for 320×240 resolution, with a P-IV PC running at 3GHz). Chapter 5 addresses surveillance and presents a full real-time behavior-analysis framework, featuring layers at pixel, object, event and visualization level. More specifically, this framework captures the human motion, classifies its posture, infers the semantic event exploiting interaction modeling, and performs the 3-D scene reconstruction. We have introduced our system design based on a specific software architecture, by employing the well-known "4+1" view model. In addition, human behavior analysis algorithms are directly designed for real-time operation and embedded in an experimental runtime AV content-analysis architecture. This executable system is designed to be generic for multiple streaming applications with component-based architectures. To evaluate the performance, we have applied this networked system in a single-camera setup. The experimental platform operates with two Pentium Quadcore engines (2.33 GHz) and 4-GB memory. Performance evaluations have shown that this networked framework is efficient and achieves a fast performance (13-15 frames/second) for monocular video sequences. Moreover, a dual-camera setup is tested within the behavior-analysis framework. After automatic camera calibration is conducted, the 3-D reconstruction and communication among different cameras are achieved. The extra view in the multi-camera setup improves the human tracking and event detection in case of occlusion. This extension of multiple-view fusion improves the event-based semantic analysis by 8.3-16.7% in accuracy rate. The detailed studies of two experimental intelligent applications, i.e., tennis sports analysis and surveillance, have proven their value in several extensive tests in the framework of the European Candela and Cantata ITEA research programs, where our proposed system has demonstrated competitive performance with respect to accuracy and efficiency

Smartphone picture organization: a hierarchical approach

We live in a society where the large majority of the population has a camera-equipped smartphone. In addition, hard drives and cloud storage are getting cheaper and cheaper, leading to a tremendous growth in stored personal photos. Unlike photo collections captured by a digital camera, which typically are pre-processed by the user who organizes them into event-related folders, smartphone pictures are automatically stored in the cloud. As a consequence, photo collections captured by a smartphone are highly unstructured and because smartphones are ubiquitous, they present a larger variability compared to pictures captured by a digital camera. To solve the need of organizing large smartphone photo collections automatically, we propose here a new methodology for hierarchical photo organization into topics and topic-related categories. Our approach successfully estimates latent topics in the pictures by applying probabilistic Latent Semantic Analysis, and automatically assigns a name to each topic by relying on a lexical database. Topic-related categories are then estimated by using a set of topic-specific Convolutional Neuronal Networks. To validate our approach, we ensemble and make public a large dataset of more than 8,000 smartphone pictures from 40 persons. Experimental results demonstrate major user satisfaction with respect to state of the art solutions in terms of organization.Peer ReviewedPreprin

First impressions: A survey on vision-based apparent personality trait analysis

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Personality analysis has been widely studied in psychology, neuropsychology, and signal processing fields, among others. From the past few years, it also became an attractive research area in visual computing. From the computational point of view, by far speech and text have been the most considered cues of information for analyzing personality. However, recently there has been an increasing interest from the computer vision community in analyzing personality from visual data. Recent computer vision approaches are able to accurately analyze human faces, body postures and behaviors, and use these information to infer apparent personality traits. Because of the overwhelming research interest in this topic, and of the potential impact that this sort of methods could have in society, we present in this paper an up-to-date review of existing vision-based approaches for apparent personality trait recognition. We describe seminal and cutting edge works on the subject, discussing and comparing their distinctive features and limitations. Future venues of research in the field are identified and discussed. Furthermore, aspects on the subjectivity in data labeling/evaluation, as well as current datasets and challenges organized to push the research on the field are reviewed.Peer ReviewedPostprint (author's final draft

Unmanned ground operations using semantic image segmentation through a Bayesian network

This paper discusses the machine vision element of a system designed to allow automated taxiing for Unmanned Aerial System (UAS) around civil aerodromes. The purpose of the computer vision system is to provide direct sensor data which can be used to validate vehicle position, in addition to detect potential collision risks. This is achieved through the use of a singular monocular sensor. Untrained clustering is used to segment the visual feed before descriptors of each cluster (primarily colour and texture) are then used to estimate the class. As the competency of each individual estimate can vary based on multiple factors (number of pixels, lighting conditions and even surface type) a Bayesian network is used to perform probabilistic data fusion, in order to improve the classification results. This result is shown to perform accurate image segmentation in real-world conditions, providing information viable for map matching

Multi modal multi-semantic image retrieval

PhDThe rapid growth in the volume of visual information, e.g. image, and video can overwhelm users’ ability to find and access the specific visual information of interest to them. In recent years, ontology knowledge-based (KB) image information retrieval techniques have been adopted into in order to attempt to extract knowledge from these images, enhancing the retrieval performance. A KB framework is presented to promote semi-automatic annotation and semantic image retrieval using multimodal cues (visual features and text captions). In addition, a hierarchical structure for the KB allows metadata to be shared that supports multi-semantics (polysemy) for concepts. The framework builds up an effective knowledge base pertaining to a domain specific image collection, e.g. sports, and is able to disambiguate and assign high level semantics to ‘unannotated’ images. Local feature analysis of visual content, namely using Scale Invariant Feature Transform (SIFT) descriptors, have been deployed in the ‘Bag of Visual Words’ model (BVW) as an effective method to represent visual content information and to enhance its classification and retrieval. Local features are more useful than global features, e.g. colour, shape or texture, as they are invariant to image scale, orientation and camera angle. An innovative approach is proposed for the representation, annotation and retrieval of visual content using a hybrid technique based upon the use of an unstructured visual word and upon a (structured) hierarchical ontology KB model. The structural model facilitates the disambiguation of unstructured visual words and a more effective classification of visual content, compared to a vector space model, through exploiting local conceptual structures and their relationships. The key contributions of this framework in using local features for image representation include: first, a method to generate visual words using the semantic local adaptive clustering (SLAC) algorithm which takes term weight and spatial locations of keypoints into account. Consequently, the semantic information is preserved. Second a technique is used to detect the domain specific ‘non-informative visual words’ which are ineffective at representing the content of visual data and degrade its categorisation ability. Third, a method to combine an ontology model with xi a visual word model to resolve synonym (visual heterogeneity) and polysemy problems, is proposed. The experimental results show that this approach can discover semantically meaningful visual content descriptions and recognise specific events, e.g., sports events, depicted in images efficiently. Since discovering the semantics of an image is an extremely challenging problem, one promising approach to enhance visual content interpretation is to use any associated textual information that accompanies an image, as a cue to predict the meaning of an image, by transforming this textual information into a structured annotation for an image e.g. using XML, RDF, OWL or MPEG-7. Although, text and image are distinct types of information representation and modality, there are some strong, invariant, implicit, connections between images and any accompanying text information. Semantic analysis of image captions can be used by image retrieval systems to retrieve selected images more precisely. To do this, a Natural Language Processing (NLP) is exploited firstly in order to extract concepts from image captions. Next, an ontology-based knowledge model is deployed in order to resolve natural language ambiguities. To deal with the accompanying text information, two methods to extract knowledge from textual information have been proposed. First, metadata can be extracted automatically from text captions and restructured with respect to a semantic model. Second, the use of LSI in relation to a domain-specific ontology-based knowledge model enables the combined framework to tolerate ambiguities and variations (incompleteness) of metadata. The use of the ontology-based knowledge model allows the system to find indirectly relevant concepts in image captions and thus leverage these to represent the semantics of images at a higher level. Experimental results show that the proposed framework significantly enhances image retrieval and leads to narrowing of the semantic gap between lower level machinederived and higher level human-understandable conceptualisation

Machine vision for UAS ground operations: using semantic segmentation with a bayesian network classifier

This paper discusses the machine vision element of a system designed to allow Unmanned Aerial System (UAS) to perform automated taxiing around civil aerodromes, with only a monocular camera. The purpose of the computer vision system is to provide direct sensor data which can be used to validate vehicle position, in addition to detecting potential collision risks. In practice, untrained clustering is used to segment the visual feed before descriptors of each cluster (primarily colour and texture) are used to estimate the class. As the competency of each individual estimate can vary dependent on multiple factors (number of pixels, lighting conditions and even surface type). A Bayesian network is used to perform probabilistic data fusion, in order to improve the classification results. This result is shown to perform accurate image segmentation in real-world conditions, providing information viable for localisation and obstacle detection