Detecting potential falling objects by inferring human action and natural disturbance

By Bo Zheng, Yibiao Zhao, Joey C. Yu, Katsushi Ikeuchi, Song-Chun Zhu. 2014

43 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Artificial Intelligence, Computer Vision and Pattern Recognition

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Citations in the corpus

Latent Space Physics: Towards Learning the Temporal Evolution of Fluid Flow (2018, background): “While physical properties and interactions of objects have long been of interest for computer vision problems [24, 34, 13], addressing physics problems with deep learning algorithms is an area of particular interest in recent years.”
"What Happens If..." Learning to Predict the Effect of Forces in Images (2016, background): “[44] detect potentially falling objects given a point cloud representing the scene.”
Newtonian Image Understanding: Unfolding the Dynamics of Objects in Static Images (2015, background): “The approach of [38] computes the probability that an object falls based on inferring disturbances caused naturally or by human actions.”
Physics 101: Learning Physical Object Properties from Unlabeled Videos (2016, methodology): “Our incorporation of physical laws into our model distinguish our framework from previous methods introduced in the computer vision and robotics community for predicting physical interactions or properties of objects for various purposes [4, 5, 9, 12, 16, 21, 22, 26].”
Semantically-enriched 3D models for common-sense knowledge (2015, neutral): “Our key insight is that many aspects of these properties are reflected strongly in the contextualized observations of 3D models within 3D scenes composed of object models (e.g., a living room with tables, chairs, couch, TV, etc.) We use a 3D scene dataset of common interior environments such as…”
Scene Understanding by Reasoning Stability and Safety (2015, methodology): “In our recent work (Zheng et al. 2013, 2014), we propose a novel intuitive physics model based on gravity potential energy transfer.”
Automatic Understanding of Image and Video Advertisements (2017, background): “There is initial work in understanding physical forces [53, 52, 86] and object transformations [26], but this work is still in its infancy and is not sufficient for full physical process understanding, as we need for many ads.”
Dark, Beyond Deep: A Paradigm Shift to Cognitive AI with Humanlike Common Sense (2020, background): “jects should be physically stable and safe with respect to gravity and various other potential disturbances [97, 81, 98], such as an earthquake, a gust of wind, or the actions of other humans.”
PiGraphs (2016, background): “The seminal work on affordances by Gibson [1977] has provided inspiration for leveraging human-object interaction data in a variety of tasks, including the improvement of pose estimation, object recognition, action classification, and many other problems [Stark et al. 2008; Bohg et al. 2013; Koppula”
SceneGrok (2014, background): “There is a variety of related work that has looked at using human-object interactions to improve pose estimation, object recognition, action classification, and other related problem in different communities [Fritz et al. 2006; Montesano et al. 2008; Stark et al. 2008; Sun et al. 2010; Hermans et al”
Galileo: Perceiving Physical Object Properties by Integrating a Physics Engine with Deep Learning (2015, background): “Note that in the fields of computer vision and robotics, there have been studies on predicting physical interactions or inferring 3D properties of objects for various purposes including 3D reasoning [6, 13] and tracking [9].”