Visual Attention for Robotic Cognition: A Survey

Begum, Momotaz; Karray, Fakhri

doi:10.1109/tamd.2010.2096505

Cited by 57 publications

(31 citation statements)

References 106 publications

(194 reference statements)

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“…The saliency map is then evaluated in terms of its capacity for predicting the image regions that will be explored by covert and overt attentional shifts according to some evaluation measure [4]. The problem of eye guidance is somehow neglected or, if needed for practical purposes [8], it is solved by adopting some deterministic choice procedure. The latter is usually based on the arg max operation [9].…”

mentioning

confidence: 99%

Ecological Sampling of Gaze Shifts

Boccignone

Ferraro

2014

IEEE Trans. Cybern.

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Abstract-Visual attention guides our gaze to relevant parts of the viewed scene, yet the moment-to-moment relocation of gaze can be different among observers even though the same locations are taken into account. Surprisingly, the variability of eye movements has been so far overlooked by the great majority of computational models of visual attention. In this paper we present the ecological sampling model, a stochastic model of eye guidance explaining such variability. The gaze shift mechanism is conceived as an active random sampling that the foraging eye carries out upon the visual landscape, under the constraints set by the observable features and the global complexity of the landscape. By drawing on results reported in the foraging literature, the actual gaze relocation is eventually driven by a stochastic differential equation whose noise source is sampled from a mixture of α-stable distributions. This way, the sampling strategy proposed here allows to mimic a fundamental property of the eye guidance mechanism: where we choose to look next at any given moment in time, it is not completely deterministic, but neither is it completely random To show that the model yields gaze shift motor behaviors that exhibit statistics similar to those displayed by human observers, we compare simulation outputs with those obtained from eye-tracked subjects while viewing complex dynamic scenes.

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confidence: 99%

Ecological Sampling of Gaze Shifts

Boccignone

Ferraro

2014

IEEE Trans. Cybern.

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“…Our experiments demonstrated that the saliency model outperforms other models with respect to performance measures. This paper has addressed the bottom-up cues into the saliency model, however, in human visual system, top-down attention and the combination of the bottom-up and top-down cues play significant role in the emergence of high level intelligence [56].…”

Section: Discussionmentioning

confidence: 99%

Fusion of Saliency Maps for Visual Attention Selection in Dynamic Scenes

Xu¹,

Yue²

2013

IJARAI

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Abstract-Human vision system can optionally process the visual information and adjust the contradiction between the limited resources and the huge visual information. Building attention models similar to human visual attention system should be very beneficial to computer vision and machine intelligence; meanwhile, it has been a challenging task due to the complexity of human brain and limited understanding of the mechanisms underlying the human attention system. Previous studies emphasized on static attention, however the motion features, which are playing key roles in human attention system intuitively, have not been well integrated into the previous models. Motion features such as motion direction are assumed to be processed within the dorsal visual and the dorsal auditory pathways and there is no systematic approach to extract the motion cues well so far. In this paper, we proposed a generic Global Attention Model (GAM) system based on visual attention analysis. The computational saliency map is superimposed by a set of saliency maps via different predefined approaches. We added three saliencies maps up together to reflect dominant motion features into the attention model, i.e., the fused saliency map at each frame is adjusted by the top-down, static and motion saliency maps. By doing this, the proposed attention model accommodating motion feature into the system so that it can responds to real visual events in a manner similar to the human visual attention system in a realistic circumstance. The visual challenges used in our experiments are selected from the benchmark video sequences. We tested the GAM on several dynamic scenes, such as traffic artery, parachuter landing and surfing, with high speed and cluttered background. The experiment results showed the GAM system demonstrated high robustness and real-time ability under complex dynamic scenes. Extensive evaluations based on comparisons with other approaches of the attention model results have verified the effectiveness of the proposed system.

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“…Begum and Karray provided a detailed survey on computational models of visual attention for robot cognition [17]. In this paper, we use operational constraints imposed by a robotic platform and focus on related work with similar assumptions.…”

Section: Related Workmentioning

confidence: 99%

Joint Attention by Gaze Interpolation and Saliency

Yücel

Salah

Meriçli

et al. 2013

IEEE Trans. Cybern.

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Abstract-Joint attention, which is the ability of coordination of a common point of reference with the communicating party, emerges as a key factor in various interaction scenarios. This paper presents an image-based method for establishing joint attention between an experimenter and a robot. The precise analysis of the experimenter's eye region requires stability and high-resolution image acquisition, which is not always available. We investigate regression-based interpolation of the gaze direction from the head pose of the experimenter, which is easier to track. Gaussian process regression and neural networks are contrasted to interpolate the gaze direction. Then, we combine gaze interpolation with image-based saliency to improve the target point estimates and test three different saliency schemes. We demonstrate the proposed method on a human-robot interaction scenario. Cross-subject evaluations, as well as experiments under adverse conditions (such as dimmed or artificial illumination or motion blur), show that our method generalizes well and achieves rapid gaze estimation for establishing joint attention.

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Visual Attention for Robotic Cognition: A Survey

Cited by 57 publications

References 106 publications

Ecological Sampling of Gaze Shifts

Ecological Sampling of Gaze Shifts

Fusion of Saliency Maps for Visual Attention Selection in Dynamic Scenes

Joint Attention by Gaze Interpolation and Saliency

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