242,961 research outputs found
Episodic Reasoning for Vision-Based Human Action Recognition
Smart Spaces, Ambient Intelligence, and Ambient Assisted Living are environmental paradigms that strongly depend on their capability to recognize human actions. While most solutions rest on sensor value interpretations and video analysis applications, few have realized the importance of incorporating common-sense capabilities to support the recognition process. Unfortunately, human action recognition cannot be successfully accomplished by only analyzing body postures. On the contrary, this task should be supported by profound knowledge of human agency nature and its tight connection to the reasons and motivations that explain it. The combination of this knowledge and the knowledge about how the world works is essential for recognizing and understanding human actions without committing common-senseless mistakes. This work demonstrates the impact that episodic reasoning has in improving the accuracy of a computer vision system for human action recognition. This work also presents formalization, implementation, and evaluation details of the knowledge model that supports the episodic reasoning
Learning the Semantics of Manipulation Action
In this paper we present a formal computational framework for modeling
manipulation actions. The introduced formalism leads to semantics of
manipulation action and has applications to both observing and understanding
human manipulation actions as well as executing them with a robotic mechanism
(e.g. a humanoid robot). It is based on a Combinatory Categorial Grammar. The
goal of the introduced framework is to: (1) represent manipulation actions with
both syntax and semantic parts, where the semantic part employs
-calculus; (2) enable a probabilistic semantic parsing schema to learn
the -calculus representation of manipulation action from an annotated
action corpus of videos; (3) use (1) and (2) to develop a system that visually
observes manipulation actions and understands their meaning while it can reason
beyond observations using propositional logic and axiom schemata. The
experiments conducted on a public available large manipulation action dataset
validate the theoretical framework and our implementation
Temporal Relational Reasoning in Videos
Temporal relational reasoning, the ability to link meaningful transformations
of objects or entities over time, is a fundamental property of intelligent
species. In this paper, we introduce an effective and interpretable network
module, the Temporal Relation Network (TRN), designed to learn and reason about
temporal dependencies between video frames at multiple time scales. We evaluate
TRN-equipped networks on activity recognition tasks using three recent video
datasets - Something-Something, Jester, and Charades - which fundamentally
depend on temporal relational reasoning. Our results demonstrate that the
proposed TRN gives convolutional neural networks a remarkable capacity to
discover temporal relations in videos. Through only sparsely sampled video
frames, TRN-equipped networks can accurately predict human-object interactions
in the Something-Something dataset and identify various human gestures on the
Jester dataset with very competitive performance. TRN-equipped networks also
outperform two-stream networks and 3D convolution networks in recognizing daily
activities in the Charades dataset. Further analyses show that the models learn
intuitive and interpretable visual common sense knowledge in videos.Comment: camera-ready version for ECCV'1
Vision systems with the human in the loop
The emerging cognitive vision paradigm deals with vision systems that apply machine learning and automatic reasoning in order to learn from what they perceive. Cognitive vision systems can rate the relevance and consistency of newly acquired knowledge, they can adapt to their environment and thus will exhibit high robustness. This contribution presents vision systems that aim at flexibility and robustness. One is tailored for content-based image retrieval, the others are cognitive vision systems that constitute prototypes of visual active memories which evaluate, gather, and integrate contextual knowledge for visual analysis. All three systems are designed to interact with human users. After we will have discussed adaptive content-based image retrieval and object and action recognition in an office environment, the issue of assessing cognitive systems will be raised. Experiences from psychologically evaluated human-machine interactions will be reported and the promising potential of psychologically-based usability experiments will be stressed
Cognitive visual tracking and camera control
Cognitive visual tracking is the process of observing and understanding the behaviour of a moving person. This paper presents an efficient solution to extract, in real-time, high-level information from an observed scene, and generate the most appropriate commands for a set of pan-tilt-zoom (PTZ) cameras in a surveillance scenario. Such a high-level feedback control loop, which is the main novelty of our work, will serve to reduce uncertainties in the observed scene and to maximize the amount of information extracted from it. It is implemented with a distributed camera system using SQL tables as virtual communication channels, and Situation Graph Trees for knowledge representation, inference and high-level camera control. A set of experiments in a surveillance scenario show the effectiveness of our approach and its potential for real applications of cognitive vision
Crowdsourcing in Computer Vision
Computer vision systems require large amounts of manually annotated data to
properly learn challenging visual concepts. Crowdsourcing platforms offer an
inexpensive method to capture human knowledge and understanding, for a vast
number of visual perception tasks. In this survey, we describe the types of
annotations computer vision researchers have collected using crowdsourcing, and
how they have ensured that this data is of high quality while annotation effort
is minimized. We begin by discussing data collection on both classic (e.g.,
object recognition) and recent (e.g., visual story-telling) vision tasks. We
then summarize key design decisions for creating effective data collection
interfaces and workflows, and present strategies for intelligently selecting
the most important data instances to annotate. Finally, we conclude with some
thoughts on the future of crowdsourcing in computer vision.Comment: A 69-page meta review of the field, Foundations and Trends in
Computer Graphics and Vision, 201
- ā¦