In this paper, we propose a novel deep learning based video saliency prediction method, named DeepVS. Specifically, we establish a large-scale eye-tracking database of videos (LEDOV), which includes 32 subjects’ fixations on 538 videos. We find from LEDOV that human attention is more likely to be attracted by objects, particularly the moving objects or the moving parts of objects. Hence, an object-to-motion convolutional neural network (OM-CNN) is developed to predict the intra-frame saliency for DeepVS, which is composed of the objectness and motion subnets. In OM-CNN, cross-net mask and hierarchical feature normalization are proposed to combine the spatial features of the objectness subnet and the temporal features of the motion subnet. We further find from our database that there exists a temporal correlation of human attention with a smooth saliency transition across video frames. We thus propose saliency-structured convolutional long short-term memory (SS-ConvLSTM) network, using the extracted features from OM-CNN as the input. Consequently, the inter-frame saliency maps of a video can be generated, which consider both structured output with center-bias and cross-frame transitions of human attention maps. Finally, the experimental results show that DeepVS advances the state-of-the-art in video saliency prediction.
@cite_48 is another widely used database, designed to evaluate the contributions of different visual features on gaze clustering. DIEM comprises 84 videos sourced from publicly accessible videos including advertisement, game trailer, movie trailer and news clip. Most of these videos have frequent cinematic cuts. Each video lasts for 27-217 seconds at 30 fps. The free-viewing fixations of around 50 subjects were tracked for each video.
@cite_48
2171943915 2171525877 1910566948 1985004765
Visual object analysis researchers are increasingly experimenting with video, because it is expected that motion cues should help with detection, recognition, and other analysis tasks. This paper presents the Cambridge-driving Labeled Video Database (CamVid) as the first collection of videos with object class semantic labels, complete with metadata. The database provides ground truth labels that associate each pixel with one of 32 semantic classes. The database addresses the need for experimental data to quantitatively evaluate emerging algorithms. While most videos are filmed with fixed-position CCTV-style cameras, our data was captured from the perspective of a driving automobile. The driving scenario increases the number and heterogeneity of the observed object classes. Over 10min of high quality 30Hz footage is being provided, with corresponding semantically labeled images at 1Hz and in part, 15Hz. The CamVid Database offers four contributions that are relevant to object analysis researchers. First, the per-pixel semantic segmentation of over 700 images was specified manually, and was then inspected and confirmed by a second person for accuracy. Second, the high-quality and large resolution color video images in the database represent valuable extended duration digitized footage to those interested in driving scenarios or ego-motion. Third, we filmed calibration sequences for the camera color response and intrinsics, and computed a 3D camera pose for each frame in the sequences. Finally, in support of expanding this or other databases, we present custom-made labeling software for assisting users who wish to paint precise class-labels for other images and videos. We evaluate the relevance of the database by measuring the performance of an algorithm from each of three distinct domains: multi-class object recognition, pedestrian detection, and label propagation. In this paper we propose a novel aesthetic model emphasizing psycho-visual statistics extracted from multiple levels in contrast to earlier approaches that rely only on descriptors suited for image recognition or based on photographic principles. At the lowest level, we determine dark-channel, sharpness and eye-sensitivity statistics over rectangular cells within a frame. At the next level, we extract Sentibank features (1,200 pre-trained visual classifiers) on a given frame, that invoke specific sentiments such as "colorful clouds", "smiling face" etc. and collect the classifier responses as frame-level statistics. At the topmost level, we extract trajectories from video shots. Using viewer's fixation priors, the trajectories are labeled as foreground, and background camera on which statistics are computed. Additionally, spatio-temporal local binary patterns are computed that capture texture variations in a given shot. Classifiers are trained on individual feature representations independently. On thorough evaluation of 9 different types of features, we select the best features from each level -- dark channel, affect and camera motion statistics. Next, corresponding classifier scores are integrated in a sophisticated low-rank fusion framework to improve the final prediction scores. Our approach demonstrates strong correlation with human prediction on 1,000 broadcast quality videos released by NHK as an aesthetic evaluation dataset. With the proliferation of wearable cameras, the number of videos of users documenting their personal lives using such devices is rapidly increasing. Since such videos may span hours, there is an important need for mechanisms that represent the information content in a compact form (i.e., shorter videos which are more easily browsable sharable). Motivated by these applications, this paper focuses on the problem of egocentric video summarization. Such videos are usually continuous with significant camera shake and other quality issues. Because of these reasons, there is growing consensus that direct application of standard video summarization tools to such data yields unsatisfactory performance. In this paper, we demonstrate that using gaze tracking information (such as fixation and saccade) significantly helps the summarization task. It allows meaningful comparison of different image frames and enables deriving personalized summaries (gaze provides a sense of the camera wearer's intent). We formulate a summarization model which captures common-sense properties of a good summary, and show that it can be solved as a submodular function maximization with partition matroid constraints, opening the door to a rich body of work from combinatorial optimization. We evaluate our approach on a new gaze-enabled egocentric video dataset (over 15 hours), which will be a valuable standalone resource. Key frame based video summarization has emerged as an important area of research for the multimedia community. Video key frames enable an user to access any video in a friendly and meaningful way. In this paper, we propose an automated method of video key frame extraction using dynamic Delaunay graph clustering via an iterative edge pruning strategy. A structural constraint in form of a lower limit on the deviation ratio of the graph vertices further improves the video summary. We also employ an information-theoretic pre-sampling where significant valleys in the mutual information profile of the successive frames in a video are used to capture more informative frames. Various video key frame visualization techniques for efficient video browsing and navigation purposes are incorporated. A comprehensive evaluation on 100 videos from the Open Video and YouTube databases using both objective and subjective measures demonstrate the superiority of our key frame extraction method.