Video indexing and summarization using motion activity

In this dissertation, video-indexing techniques using low-level motion activity characteristics and their application to video summarization are presented. The MPEG-7 motion activity feature is defined as the subjective level of activity or motion in a video segment. First, a novel psychophysical and analytical framework for automatic measurement of motion activity in compliance with its subjective perception is developed. A psychophysically sound subjective ground truth for motion activity and a test-set of video clips is constructed for this purpose. A number of low-level, compressed domain motion vector based, known and novel descriptors are then described. It is shown that these descriptors successfully estimate the subjective level of motion activity of video clips. Furthermore, the individual strengths and limitations of the proposed descriptors are determined using a novel pair wise comparison framework. It is verified that the intensity of motion activity descriptor of the MPEG-7 standard is one of the best performers, while a novel descriptor proposed in this dissertation performs comparably or better. A new descriptor for the spatial distribution of motion activity in a scene is proposed. This descriptor is supplementary to the intensity of motion activity descriptor. The new descriptor is shown to have comparable query retrieval performance to the current spatial distribution of motion activity descriptor of the MPEG-7 standard. The insights obtained from the motion activity investigation are applied to video summarization. A novel approach to summarizing and skimming through video using motion activity is presented. The approach is based on allocation of playback time to video segments proportional to the motion activity of the segments. Low activity segments are played faster than high activity segments in such a way that a constant level of activity is maintained throughout the video. Since motion activity is a low-complexity descriptor, the proposed summarization techniques are extremely fast. The summarization techniques are successfully used on surveillance video, The proposed techniques can also be used as a preprocessing stage for more complex summarization and content analysis techniques, thus providing significant cost gains

Activity-driven content adaptation for effective video summarisation

In this paper, we present a novel method for content adaptation and video summarization fully implemented in compressed-domain. Firstly, summarization of generic videos is modeled as the process of extracted human objects under various activities/events. Accordingly, frames are classified into five categories via fuzzy decision including shot changes (cut and gradual transitions), motion activities (camera motion and object motion) and others by using two inter-frame measurements. Secondly, human objects are detected using Haar-like features. With the detected human objects and attained frame categories, activity levels for each frame are determined to adapt with video contents. Continuous frames belonging to same category are grouped to form one activity entry as content of interest (COI) which will convert the original video into a series of activities. An overall adjustable quota is used to control the size of generated summarization for efficient streaming purpose. Upon this quota, the frames selected for summarization are determined by evenly sampling the accumulated activity levels for content adaptation. Quantitative evaluations have proved the effectiveness and efficiency of our proposed approach, which provides a more flexible and general solution for this topic as domain-specific tasks such as accurate recognition of objects can be avoided

Machine Analysis of Facial Expressions

No abstract

General highlight detection in sport videos

Attention is a psychological measurement of human reflection against stimulus. We propose a general framework of highlight detection by comparing attention intensity during the watching of sports videos. Three steps are involved: adaptive selection on salient features, unified attention estimation and highlight identification. Adaptive selection computes feature correlation to decide an optimal set of salient features. Unified estimation combines these features by the technique of multi-resolution autoregressive (MAR) and thus creates a temporal curve of attention intensity. We rank the intensity of attention to discriminate boundaries of highlights. Such a framework alleviates semantic uncertainty around sport highlights and leads to an efficient and effective highlight detection. The advantages are as follows: (1) the capability of using data at coarse temporal resolutions; (2) the robustness against noise caused by modality asynchronism, perception uncertainty and feature mismatch; (3) the employment of Markovian constrains on content presentation, and (4) multi-resolution estimation on attention intensity, which enables the precise allocation of event boundaries

Keeping track of worm trackers

C. elegans is used extensively as a model system in the neurosciences due to its well defined nervous system. However, the seeming simplicity of this nervous system in anatomical structure and neuronal connectivity, at least compared to higher animals, underlies a rich diversity of behaviors. The usefulness of the worm in genome-wide mutagenesis or RNAi screens, where thousands of strains are assessed for phenotype, emphasizes the need for computational methods for automated parameterization of generated behaviors. In addition, behaviors can be modulated upon external cues like temperature, O2 and CO2 concentrations, mechanosensory and chemosensory inputs. Different machine vision tools have been developed to aid researchers in their efforts to inventory and characterize defined behavioral “outputs”. Here we aim at providing an overview of different worm-tracking packages or video analysis tools designed to quantify different aspects of locomotion such as the occurrence of directional changes (turns, omega bends), curvature of the sinusoidal shape (amplitude, body bend angles) and velocity (speed, backward or forward movement)

A dynamic texture based approach to recognition of facial actions and their temporal models

In this work, we propose a dynamic texture-based approach to the recognition of facial Action Units (AUs, atomic facial gestures) and their temporal models (i.e., sequences of temporal segments: neutral, onset, apex, and offset) in near-frontal-view face videos. Two approaches to modeling the dynamics and the appearance in the face region of an input video are compared: an extended version of Motion History Images and a novel method based on Nonrigid Registration using Free-Form Deformations (FFDs). The extracted motion representation is used to derive motion orientation histogram descriptors in both the spatial and temporal domain. Per AU, a combination of discriminative, frame-based GentleBoost ensemble learners and dynamic, generative Hidden Markov Models detects the presence of the AU in question and its temporal segments in an input image sequence. When tested for recognition of all 27 lower and upper face AUs, occurring alone or in combination in 264 sequences from the MMI facial expression database, the proposed method achieved an average event recognition accuracy of 89.2 percent for the MHI method and 94.3 percent for the FFD method. The generalization performance of the FFD method has been tested using the Cohn-Kanade database. Finally, we also explored the performance on spontaneous expressions in the Sensitive Artificial Listener data set