Exploiting Stereoscopic Disparity for Augmenting Human Activity Recognition Performance

This work investigates several ways to exploit scene depth information, implicitly available through the modality of stereoscopic disparity in 3D videos, with the purpose of augmenting performance in the problem of recognizing complex human activities in natural settings. The standard state-of-the-art activity recognition algorithmic pipeline consists in the consecutive stages of video description, video representation and video classification. Multimodal, depth-aware modifications to standard methods are being proposed and studied, both for video description and for video representation, that indirectly incorporate scene geometry information derived from stereo disparity. At the description level, this is made possible by suitably manipulating video interest points based on disparity data. At the representation level, the followed approach represents each video by multiple vectors corresponding to different disparity zones, resulting in multiple activity descriptions defined by disparity characteristics. In both cases, a scene segmentation is thus implicitly implemented, based on the distance of each imaged object from the camera during video acquisition. The investigated approaches are flexible and able to cooperate with any monocular low-level feature descriptor. They are evaluated using a publicly available activity recognition dataset of unconstrained stereoscopic 3D videos, consisting in extracts from Hollywood movies, and compared both against competing depth-aware approaches and a state-of-the-art monocular algorithm. Quantitative evaluation reveals that some of the examined approaches achieve state-of-the-art performance

Multimodal Stereoscopic Movie Summarization Conforming to Narrative Characteristics

Video summarization is a timely and rapidly developing research field with broad commercial interest, due to the increasing availability of massive video data. Relevant algorithms face the challenge of needing to achieve a careful balance between summary compactness, enjoyability, and content coverage. The specific case of stereoscopic 3D theatrical films has become more important over the past years, but not received corresponding research attention. In this paper, a multi-stage, multimodal summarization process for such stereoscopic movies is proposed, that is able to extract a short, representative video skim conforming to narrative characteristics from a 3D film. At the initial stage, a novel, low-level video frame description method is introduced (frame moments descriptor) that compactly captures informative image statistics from luminance, color, optical flow, and stereoscopic disparity video data, both in a global and in a local scale. Thus, scene texture, illumination, motion, and geometry properties may succinctly be contained within a single frame feature descriptor, which can subsequently be employed as a building block in any key-frame extraction scheme, e.g., for intra-shot frame clustering. The computed key-frames are then used to construct a movie summary in the form of a video skim, which is post-processed in a manner that also considers the audio modality. The next stage of the proposed summarization pipeline essentially performs shot pruning, controlled by a user-provided shot retention parameter, that removes segments from the skim based on the narrative prominence of movie characters in both the visual and the audio modalities. This novel process (multimodal shot pruning) is algebraically modeled as a multimodal matrix column subset selection problem, which is solved using an evolutionary computing approach. Subsequently, disorienting editing effects induced by summarization are dealt with, through manipulation of the video skim. At the last step, the skim is suitably post-processed in order to reduce stereoscopic video defects that may cause visual fatigue