Activity Recognition based on a Magnitude-Orientation Stream Network

The temporal component of videos provides an important clue for activity recognition, as a number of activities can be reliably recognized based on the motion information. In view of that, this work proposes a novel temporal stream for two-stream convolutional networks based on images computed from the optical flow magnitude and orientation, named Magnitude-Orientation Stream (MOS), to learn the motion in a better and richer manner. Our method applies simple nonlinear transformations on the vertical and horizontal components of the optical flow to generate input images for the temporal stream. Experimental results, carried on two well-known datasets (HMDB51 and UCF101), demonstrate that using our proposed temporal stream as input to existing neural network architectures can improve their performance for activity recognition. Results demonstrate that our temporal stream provides complementary information able to improve the classical two-stream methods, indicating the suitability of our approach to be used as a temporal video representation.Comment: 8 pages, SIBGRAPI 201

Pick and Place Without Geometric Object Models

We propose a novel formulation of robotic pick and place as a deep reinforcement learning (RL) problem. Whereas most deep RL approaches to robotic manipulation frame the problem in terms of low level states and actions, we propose a more abstract formulation. In this formulation, actions are target reach poses for the hand and states are a history of such reaches. We show this approach can solve a challenging class of pick-place and regrasping problems where the exact geometry of the objects to be handled is unknown. The only information our method requires is: 1) the sensor perception available to the robot at test time; 2) prior knowledge of the general class of objects for which the system was trained. We evaluate our method using objects belonging to two different categories, mugs and bottles, both in simulation and on real hardware. Results show a major improvement relative to a shape primitives baseline

Context-Independent Task Knowledge for Neurosymbolic Reasoning in Cognitive Robotics

One of the current main goals of artificial intelligence and robotics research is the creation of an artificial assistant which can have flexible, human like behavior, in order to accomplish everyday tasks. A lot of what is context-independent task knowledge to the human is what enables this flexibility at multiple levels of cognition. In this scope the author analyzes how to acquire, represent and disambiguate symbolic knowledge representing context-independent task knowledge, abstracted from multiple instances: this thesis elaborates the incurred problems, implementation constraints, current state-of-the-art practices and ultimately the solutions newly introduced in this scope. The author specifically discusses acquisition of context-independent task knowledge from large amounts of human-written texts and their reusability in the robotics domain; the acquisition of knowledge on human musculoskeletal dependencies constraining motion which allows a better higher level representation of observed trajectories; the means of verbalization of partial contextual and instruction knowledge, increasing interaction possibilities with the human as well as contextual adaptation. All the aforementioned points are supported by evaluation in heterogeneous setups, to bring a view on how to make optimal use of statistical & symbolic applications (i.e. neurosymbolic reasoning) in cognitive robotics. This work has been performed to enable context-adaptable artificial assistants, by bringing together knowledge on what is usually regarded as context-independent task knowledge

Action Recognition in Videos: from Motion Capture Labs to the Web

This paper presents a survey of human action recognition approaches based on visual data recorded from a single video camera. We propose an organizing framework which puts in evidence the evolution of the area, with techniques moving from heavily constrained motion capture scenarios towards more challenging, realistic, "in the wild" videos. The proposed organization is based on the representation used as input for the recognition task, emphasizing the hypothesis assumed and thus, the constraints imposed on the type of video that each technique is able to address. Expliciting the hypothesis and constraints makes the framework particularly useful to select a method, given an application. Another advantage of the proposed organization is that it allows categorizing newest approaches seamlessly with traditional ones, while providing an insightful perspective of the evolution of the action recognition task up to now. That perspective is the basis for the discussion in the end of the paper, where we also present the main open issues in the area.Comment: Preprint submitted to CVIU, survey paper, 46 pages, 2 figures, 4 table