In this paper, we shall consider the problem of deploying attention to the subsets of the video streams for collating the most relevant data and information of interest related to a given task. We formalize this monitoring problem as a foraging problem. We propose a probabilistic framework to model observer’s attentive behavior as the behavior of a forager. The forager, moment to moment, focuses its attention on the most informative stream camera, detects interesting objects or activities, or switches to a more profitable stream. The approach proposed here is suitable to be exploited for multistream video summarization. Meanwhile, it can serve as a preliminary step for more sophisticated video surveillance, e.g., activity and behavior analysis. Experimental results achieved on the UCR Videoweb Activities Data Set, a publicly available data set, are presented to illustrate the utility of the proposed technique.
Second, the visual attention problem is formulated as a foraging problem by extending previous work on L 'evy flights as a prior for sampling gaze shift amplitudes @cite_48 , which mainly relied on bottom-up salience. At the same time, task dependence is introduced, which is not achieved through ad hoc procedures. It is naturally integrated within attentional mechanisms in terms of rewards experienced in the attentive stage when the stream is explored. This issue is seldom taken into account in computational models of visual attention (see @cite_25 @cite_13 but in particular Tatler @cite_42 ). A preliminary study on this challenging problem has been presented in @cite_55 , but limited to the task of searching for text in static images.
@cite_48 @cite_55 @cite_42 @cite_13 @cite_25
2017836770 2144764737 2313180542 2129053638
Visual attention is the cognitive process of directing our gaze on one aspect of the visual field while ignoring others. The mainstream approach to modeling focal visual attention involves identifying saliencies in the image and applying a search process to the salient regions. However, such inference schemes commonly fail to accurately capture perceptual attractors, require massive computational effort and, generally speaking, are not biologically plausible. This paper introduces a novel approach to the problem of visual search by framing it as an adaptive learning process. In particular, we devise an approximate optimal control framework, based on reinforcement learning, for actively searching a visual field. We apply the method to the problem of face detection and demonstrate that the technique is both accurate and scalable. Moreover, the foundations proposed here pave the way for extending the approach to other large-scale visual perception problems. Five important trends have emerged from recent work on computational models of focal visual attention that emphasize the bottom-up, image-based control of attentional deployment. First, the perceptual saliency of stimuli critically depends on the surrounding context. Second, a unique 'saliency map' that topographically encodes for stimulus conspicuity over the visual scene has proved to be an efficient and plausible bottom-up control strategy. Third, inhibition of return, the process by which the currently attended location is prevented from being attended again, is a crucial element of attentional deployment. Fourth, attention and eye movements tightly interplay, posing computational challenges with respect to the coordinate system used to control attention. And last, scene understanding and object recognition strongly constrain the selection of attended locations. Insights from these five key areas provide a framework for a computational and neurobiological understanding of visual attention. In many computer vision tasks, the relevant information to solve the problem at hand is mixed to irrelevant, distracting information. This has motivated researchers to design attentional models that can dynamically focus on parts of images or videos that are salient, e.g., by down-weighting irrelevant pixels. In this work, we propose a spatiotemporal attentional model that learns where to look in a video directly from human fixation data. We model visual attention with a mixture of Gaussians at each frame. This distribution is used to express the probability of saliency for each pixel. Time consistency in videos is modeled hierarchically by: 1) deep 3D convolutional features to represent spatial and short-term time relations and 2) a long short-term memory network on top that aggregates the clip-level representation of sequential clips and therefore expands the temporal domain from few frames to seconds. The parameters of the proposed model are optimized via maximum likelihood estimation using human fixations as training data, without knowledge of the action in each video. Our experiments on Hollywood2 show state-of-the-art performance on saliency prediction for video. We also show that our attentional model trained on Hollywood2 generalizes well to UCF101 and it can be leveraged to improve action classification accuracy on both datasets. Several visual attention models have been proposed for describing eye movements over simple stimuli and tasks such as free viewing or visual search. Yet, to date, there exists no computational framework that can reliably mimic human gaze behavior in more complex environments and tasks such as urban driving. In addition, benchmark datasets, scoring techniques, and top-down model architectures are not yet well understood. In this paper, we describe new task-dependent approaches for modeling top-down overt visual attention based on graphical models for probabilistic inference and reasoning. We describe a dynamic Bayesian network that infers probability distributions over attended objects and spatial locations directly from observed data. Probabilistic inference in our model is performed over object-related functions that are fed from manual annotations of objects in video scenes or by state-of-the-art object detection recognition algorithms. Evaluating over approximately 3 h (approximately 315 000 eye fixations and 12 000 saccades) of observers playing three video games (time-scheduling, driving, and flight combat), we show that our approach is significantly more predictive of eye fixations compared to: 1) simpler classifier-based models also developed here that map a signature of a scene (multimodal information from gist, bottom-up saliency, physical actions, and events) to eye positions; 2) 14 state-of-the-art bottom-up saliency models; and 3) brute-force algorithms such as mean eye position. Our results show that the proposed model is more effective in employing and reasoning over spatio-temporal visual data compared with the state-of-the-art.