Evaluating Multimedia Features and Fusion for Example-Based Event Detection

Multimedia event detection (MED) is a challenging problem because of the heterogeneous content and variable quality found in large collections of Internet videos. To study the value of multimedia features and fusion for representing and learning events from a set of example video clips, we created SESAME, a system for video SEarch with Speed and Accuracy for Multimedia Events. SESAME includes multiple bag-of-words event classifiers based on single data types: low-level visual, motion, and audio features; high-level semantic visual concepts; and automatic speech recognition. Event detection performance was evaluated for each event classifier. The performance of low-level visual and motion features was improved by the use of difference coding. The accuracy of the visual concepts was nearly as strong as that of the low-level visual features. Experiments with a number of fusion methods for combining the event detection scores from these classifiers revealed that simple fusion methods, such as arithmetic mean, perform as well as or better than other, more complex fusion methods. SESAME’s performance in the 2012 TRECVID MED evaluation was one of the best reported

Every Moment Counts: Dense Detailed Labeling of Actions in Complex Videos

Every moment counts in action recognition. A comprehensive understanding of human activity in video requires labeling every frame according to the actions occurring, placing multiple labels densely over a video sequence. To study this problem we extend the existing THUMOS dataset and introduce MultiTHUMOS, a new dataset of dense labels over unconstrained internet videos. Modeling multiple, dense labels benefits from temporal relations within and across classes. We define a novel variant of long short-term memory (LSTM) deep networks for modeling these temporal relations via multiple input and output connections. We show that this model improves action labeling accuracy and further enables deeper understanding tasks ranging from structured retrieval to action prediction.Comment: To appear in IJC

TagBook: A Semantic Video Representation without Supervision for Event Detection

We consider the problem of event detection in video for scenarios where only few, or even zero examples are available for training. For this challenging setting, the prevailing solutions in the literature rely on a semantic video representation obtained from thousands of pre-trained concept detectors. Different from existing work, we propose a new semantic video representation that is based on freely available social tagged videos only, without the need for training any intermediate concept detectors. We introduce a simple algorithm that propagates tags from a video's nearest neighbors, similar in spirit to the ones used for image retrieval, but redesign it for video event detection by including video source set refinement and varying the video tag assignment. We call our approach TagBook and study its construction, descriptiveness and detection performance on the TRECVID 2013 and 2014 multimedia event detection datasets and the Columbia Consumer Video dataset. Despite its simple nature, the proposed TagBook video representation is remarkably effective for few-example and zero-example event detection, even outperforming very recent state-of-the-art alternatives building on supervised representations.Comment: accepted for publication as a regular paper in the IEEE Transactions on Multimedi

Combining Multiple Sensors for Event Detection of Older People

International audienceWe herein present a hierarchical model-based framework for event detection using multiple sensors. Event models combine a priori knowledge of the scene (3D geometric and semantic information, such as contextual zones and equipment) with moving objects (e.g., a Person) detected by a video monitoring system. The event models follow a generic ontology based on natural language, which allows domain experts to easily adapt them. The framework novelty lies on combining multiple sensors at decision (event) level, and handling their conflict using a proba-bilistic approach. The event conflict handling consists of computing the reliability of each sensor before their fusion using an alternative combination rule for Dempster-Shafer Theory. The framework evaluation is performed on multisensor recording of instrumental activities of daily living (e.g., watching TV, writing a check, preparing tea, organizing week intake of prescribed medication) of participants of a clinical trial for Alzheimer's disease study. Two fusion cases are presented: the combination of events (or activities) from heterogeneous sensors (RGB ambient camera and a wearable inertial sensor) following a deterministic fashion, and the combination of conflicting events from video cameras with partially overlapped field of view (a RGB-and a RGB-D-camera, Kinect). Results showed the framework improves the event detection rate in both cases

Building an enhanced vocabulary of the robot environment with a ceiling pointing camera

Mobile robots are of great help for automatic monitoring tasks in different environments. One of the first tasks that needs to be addressed when creating these kinds of robotic systems is modeling the robot environment. This work proposes a pipeline to build an enhanced visual model of a robot environment indoors. Vision based recognition approaches frequently use quantized feature spaces, commonly known as Bag of Words (BoW) or vocabulary representations. A drawback using standard BoW approaches is that semantic information is not considered as a criteria to create the visual words. To solve this challenging task, this paper studies how to leverage the standard vocabulary construction process to obtain a more meaningful visual vocabulary of the robot work environment using image sequences. We take advantage of spatio-temporal constraints and prior knowledge about the position of the camera. The key contribution of our work is the definition of a new pipeline to create a model of the environment. This pipeline incorporates (1) tracking information to the process of vocabulary construction and (2) geometric cues to the appearance descriptors. Motivated by long term robotic applications, such as the aforementioned monitoring tasks, we focus on a configuration where the robot camera points to the ceiling, which captures more stable regions of the environment. The experimental validation shows how our vocabulary models the environment in more detail than standard vocabulary approaches, without loss of recognition performance. We show different robotic tasks that could benefit of the use of our visual vocabulary approach, such as place recognition or object discovery. For this validation, we use our publicly available data-set

Combining Multiple Sensors for Event Detection of Older People

International audienceWe herein present a hierarchical model-based framework for event detection using multiple sensors. Event models combine a priori knowledge of the scene (3D geometric and semantic information, such as contextual zones and equipment) with moving objects (e.g., a Person) detected by a video monitoring system. The event models follow a generic ontology based on natural language, which allows domain experts to easily adapt them. The framework novelty lies on combining multiple sensors at decision (event) level, and handling their conflict using a proba-bilistic approach. The event conflict handling consists of computing the reliability of each sensor before their fusion using an alternative combination rule for Dempster-Shafer Theory. The framework evaluation is performed on multisensor recording of instrumental activities of daily living (e.g., watching TV, writing a check, preparing tea, organizing week intake of prescribed medication) of participants of a clinical trial for Alzheimer's disease study. Two fusion cases are presented: the combination of events (or activities) from heterogeneous sensors (RGB ambient camera and a wearable inertial sensor) following a deterministic fashion, and the combination of conflicting events from video cameras with partially overlapped field of view (a RGB-and a RGB-D-camera, Kinect). Results showed the framework improves the event detection rate in both cases

MDRED: Multi-Modal Multi-Task Distributed Recognition for Event Detection

Title from PDF of title page viewed September 28, 2018Thesis advisor: Yugyung LeeVitaIncludes bibliographical references (pages 63-67)Thesis (M.S.)--School of Computing and Engineering. University of Missouri--Kansas City, 2018Understanding users’ context is essential in emerging mobile sensing applications, such as Metal Detector, Glint Finder, Facefirst. Over the last decade, Machine Learning (ML) techniques have evolved dramatically for real-world applications. Specifically, Deep Learning (DL) has attracted tremendous attention for diverse applications including speech recognition, computer vision. However, ML requires extensive computing resources. ML applications are not suitable for devices with limited computing capabilities. Furthermore, customizing ML applications for users’ context is not easy. Such a situation presents real challenges to mobile based ML applications. We are motivated to solve this problem by designing a distributed and collaborative computing framework for ML edge computing and applications. In this thesis, we propose the Multi-Modal Multi-Task Distributed Recognition for Event Detection (MDRED) framework for complex event recognition with images. The MDRED framework is based on a hybrid ML model that is composed of Deep Learning (DL) and Shallow Learning (SL). The lower level of the MDRED framework is based on the DL models for (1) object detection, (2) color recognition, (3) emotion recognition, (4) face detection, (5) text detection with event images. The higher level is based on the SL-based fusion techniques for the event detection based on the outcomes from the lower level DL models. The fusion model is designed as a weighted feature vector generated by a modified Term Frequency and Inverse Document Frequency (TF-IDF) algorithm, considering common and unique multi-modal features that are recognized for event detection. The prototype of the MDRED framework has been implemented: A master-slave architecture was designed for coordinating the distributed computing among multiple mobile devices at the edge while connecting the edge devices to the cloud ML servers. The MDRED model has been evaluated with the benchmark event datasets and compared with the state-of-the-art event detection models. The MDRED accuracy of 90.5%, 98.8%, 78% for SocEID, UIUC Sports, RED Events datasets, respectively, outperformed the baseline models of AlexNet-fc7, WEBLY-fc7, WIDER-fc7 and Event concepts. We also demonstrate the MDRED application running on Android devices for the real-time event detection.Introduction -- Background and related work -- Proposed work -- Results and evaluation -- Conclusion and future wor

Deliverable D1.4 Visual, text and audio information analysis for hypervideo, final release

Having extensively evaluated the performance of the technologies included in the first release of WP1 multimedia analysis tools, using content from the LinkedTV scenarios and by participating in international benchmarking activities, concrete decisions regarding the appropriateness and the importance of each individual method or combination of methods were made, which, combined with an updated list of information needs for each scenario, led to a new set of analysis requirements that had to be addressed through the release of the final set of analysis techniques of WP1. To this end, coordinated efforts on three directions, including (a) the improvement of a number of methods in terms of accuracy and time efficiency, (b) the development of new technologies and (c) the definition of synergies between methods for obtaining new types of information via multimodal processing, resulted in the final bunch of multimedia analysis methods for video hyperlinking. Moreover, the different developed analysis modules have been integrated into a web-based infrastructure, allowing the fully automatic linking of the multitude of WP1 technologies and the overall LinkedTV platform

Evaluating Multimedia Features and Fusion for Example-Based Event Detection

Multimedia event detection (MED) is a challenging problem because of the heterogeneous content and variable quality found in large collections of Internet videos. To study the value of multimedia features and fusion for representing and learning events from a set of example video clips, we created SESAME, a system for video SEarch with Speed and Accuracy for Multimedia Events. SESAME includes multiple bag-of-words event classifiers based on single data types: low-level visual, motion, and audio features; high-level semantic visual concepts; and automatic speech recognition. Event detection performance was evaluated for each event classifier. The performance of low-level visual and motion features was improved by the use of difference coding. The accuracy of the visual concepts was nearly as strong as that of the low-level visual features. Experiments with a number of fusion methods for combining the event detection scores from these classifiers revealed that simple fusion methods, such as arithmetic mean, perform as well as or better than other, more complex fusion methods. SESAME’s performance in the 2012 TRECVID MED evaluation was one of the best reported