Leveraging over depth in egocentric activity recognition

Activity recognition from first person videos is a growing research area. The increasing diffusion of egocentric sensors in various devices makes it timely to develop approaches able to recognize fine grained first person actions like picking up, putting down, pouring and so forth. While most of previous work focused on RGB data, some authors pointed out the importance of leveraging over depth information in this domain. In this paper we follow this trend and we propose the first deep architecture that uses depth maps as an attention mechanism for first person activity recognition. Specifically, we blend together the RGB and depth data, so to obtain an enriched input for the network. This blending puts more or less emphasis on different parts of the image based on their distance from the observer, hence acting as an attention mechanism. To further strengthen the proposed activity recognition protocol, we opt for a self labeling approach. This, combined with a Conv-LSTM block for extracting temporal information from the various frames, leads to the new state of the art on two publicly available benchmark databases. An ablation study completes our experimental findings, confirming the effectiveness of our approac

Egocentric Scene Understanding via Multimodal Spatial Rectifier

In this paper, we study a problem of egocentric scene understanding, i.e., predicting depths and surface normals from an egocentric image. Egocentric scene understanding poses unprecedented challenges: (1) due to large head movements, the images are taken from non-canonical viewpoints (i.e., tilted images) where existing models of geometry prediction do not apply; (2) dynamic foreground objects including hands constitute a large proportion of visual scenes. These challenges limit the performance of the existing models learned from large indoor datasets, such as ScanNet and NYUv2, which comprise predominantly upright images of static scenes. We present a multimodal spatial rectifier that stabilizes the egocentric images to a set of reference directions, which allows learning a coherent visual representation. Unlike unimodal spatial rectifier that often produces excessive perspective warp for egocentric images, the multimodal spatial rectifier learns from multiple directions that can minimize the impact of the perspective warp. To learn visual representations of the dynamic foreground objects, we present a new dataset called EDINA (Egocentric Depth on everyday INdoor Activities) that comprises more than 500K synchronized RGBD frames and gravity directions. Equipped with the multimodal spatial rectifier and the EDINA dataset, our proposed method on single-view depth and surface normal estimation significantly outperforms the baselines not only on our EDINA dataset, but also on other popular egocentric datasets, such as First Person Hand Action (FPHA) and EPIC-KITCHENS.Comment: Appearing in the Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 202

Regional Attention with Architecture-Rebuilt 3D Network for RGB-D Gesture Recognition

Human gesture recognition has drawn much attention in the area of computer vision. However, the performance of gesture recognition is always influenced by some gesture-irrelevant factors like the background and the clothes of performers. Therefore, focusing on the regions of hand/arm is important to the gesture recognition. Meanwhile, a more adaptive architecture-searched network structure can also perform better than the block-fixed ones like Resnet since it increases the diversity of features in different stages of the network better. In this paper, we propose a regional attention with architecture-rebuilt 3D network (RAAR3DNet) for gesture recognition. We replace the fixed Inception modules with the automatically rebuilt structure through the network via Neural Architecture Search (NAS), owing to the different shape and representation ability of features in the early, middle, and late stage of the network. It enables the network to capture different levels of feature representations at different layers more adaptively. Meanwhile, we also design a stackable regional attention module called dynamic-static Attention (DSA), which derives a Gaussian guidance heatmap and dynamic motion map to highlight the hand/arm regions and the motion information in the spatial and temporal domains, respectively. Extensive experiments on two recent large-scale RGB-D gesture datasets validate the effectiveness of the proposed method and show it outperforms state-of-the-art methods. The codes of our method are available at: https://github.com/zhoubenjia/RAAR3DNet.Comment: Accepted by AAAI 202

ENIGMA-51: Towards a Fine-Grained Understanding of Human-Object Interactions in Industrial Scenarios

ENIGMA-51 is a new egocentric dataset acquired in a real industrial domain by 19 subjects who followed instructions to complete the repair of electrical boards using industrial tools (e.g., electric screwdriver) and electronic instruments (e.g., oscilloscope). The 51 sequences are densely annotated with a rich set of labels that enable the systematic study of human-object interactions in the industrial domain. We provide benchmarks on four tasks related to human-object interactions: 1) untrimmed action detection, 2) egocentric human-object interaction detection, 3) short-term object interaction anticipation and 4) natural language understanding of intents and entities. Baseline results show that the ENIGMA-51 dataset poses a challenging benchmark to study human-object interactions in industrial scenarios. We publicly release the dataset at: https://iplab.dmi.unict.it/ENIGMA-51/