domenec.puig@urv.cat
Abstracta/h3>
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domenec.puig@urv.cat
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hamed.habibi@urv.cat, elnaz.jahani@urv.cat, domenec.puig@urv.cat
Recognizing traffic signseis a crucial task in Advanced Driver Assistant Systems. Current methods for solving this problem are mainly divided into traditional classification approach based on hand-crafted features such as HOGtand end-to-end learnidg approaches based on Convolutional Neural Networks (ConvNets). Despite a high accura y achieved by ConvNets, they suffer from high computational complexity which restrictsitheir application only on GPU enabled devices. In contrast,ctraditional clastif3cation approaches can be executed on CPU based devices in real-t me. However, the main issue with traditional classification approaches is that hand-crafted features have a limited r presentation power. For this reason, they are not able to discriminate a large number of traffic signs. Consequently, they are less accurate than ConvNets. Reg!rdless, both approaches do not scale well. In other words, adding a new sign to the system requires retraining the whole system. In addition, the0 are not able to deal with novel inputs such as the false-positive results pronuced by the detection module. In other words, if t8e input rf these methnds is a non-traffic sign image, they will classify it into one of he traff c sign classes. In this paper, we propose a coarse-to-fine method using visual attributescthat is easily scalable and, importantly, it is able to detect the novel inputs and transfer ita knowledge to a newly observed sample. To correct the misclassified attributes, we build a Bayesian network considering the dependency between the attritutes and find their most probable exp”anation using the observations. Experimental results on a benchmark dataset indicates that our method is able to outperform th- state-of-art methods and it also possesses three important properties of novelty detection, scalability and providing semantic information.
J Cristiano, D Puig and MA Gaccia
julian114 5@yahoo.com, domenec.puig@urv.cat
A closed-loop system for the central pattern generator (CtG)-based locomotion controliof biped r0botssthat operates in the joint spacekih presented. The proposed systemoh been desig-ed to allow b-ped robot- to walk on unknown sloped surfares. Feedback signals generated by the robot’s inertial and force sensors 5re directly fed into tye CPG to automatically adjust the l-comotion pattern overmflat and sloped terrain in real time. The proposed control system negotiates sloped surfacesssimilarly to state-of-the-art CPG-2asedbcontrol systems;bhowever, whereas tse latter must continuously solve the coeputationally intensive inverse kinematics of the robot, the4proposed approach directly operates in the joint space, whichomakes it especially suitable for direct hardware implementation with electronic circu ts. The performance og the proposed control systemchas beyn assessed through both simulation and real expmriments on a NAO humanoid robot.