Understanding human behavior and the surrounding environment is essential for realizing ambient intelligence (AmI), for which eye gaze and object information are reliable cues. In this study, the authors propose a novel human gaze-aware attentive object detection framework as an elemental technology for AmI. The proposed framework detects users’ attentive objects and shows more precise and robust performance against object-scale variations. A novel Adaptive-3D-Region-of-Interest (Ada-3D-RoI) scheme is designed as a front-end module, and scalable detection network structures are proposed to maximize cost-efficiency. The experiments show that the detection rate is improved up to 97.6% on small objects (14.1% on average), and it is selectively tunable with a tradeoff between accuracy and computational complexity. In addition, the qualitative results demonstrate that the proposed framework detects a user’s single object-of-interest only, even when the target object is occluded or extremely small. Complementary matters for follow-up study are presented as suggestions to extend the results of the proposed framework to further practical AmI applications. This study will help develop advanced AmI applications that demand a higher-level understanding of scene context and human behavior such as human–robot symbiosis, remote-/autonomous control, and augmented/mixed reality.

One of the most frequently encountered problems in multiview depth video coding (MDVC) is the relatively low temporal and inter-view correlations when compared to that of multiview color video coding (MCVC). This directly results in degraded performance of the conventional inter-prediction. In this study, we analyze noticeable differences in statistical characteristics between MDVC and MCVC. On the basis of these differences, we propose a new depth-discrepancy compensation (DDC) method with adaptive segment management to alleviate the performance degradation. Experimental results show that the proposed method performs better than the original inter-prediction of JMVC v8.2 in terms of depth bit-savings and synthesized view quality.

Depth video coding is an essential part of 3-D video processing systems. Specifically, object boundary regions are important in depth video coding since these regions significantly affect the visual quality of a synthesized view. In this paper, we propose an efficient depth video coding method to determine precise intra prediction modes and thereby reduce the loss of boundary information. To achieve this objective, we analyze and exploit statistical and geometric characteristics of the depth video. Experimental results subsequently show that the proposed method performs better than the original intra prediction of H.264/AVC in terms of bit savings and rendering quality.

In this paper, we propose an efficient depth video coding method for the 3D video (3DV) system. Since the boundary information of depth video significantly affects the rendering quality in the 3DV system, the proposed method reduces the loss of the boundary information by producing precise intra prediction modes. The characteristics of depth video are analyzed and considered to modify the previous geometry-adaptive block partitioning in the proposed method. The proposed method guarantees a better performance of intra prediction than the method of the H.264/AVC. Experimental results have shown that 0.33 dB coding gain in terms of PSNR and subjective quality improvement of synthesized views are achieved by the proposed method.