Hamed Habibi Aghdam, Elnaz Jahani Heravi and Domenec Puig

hamed.habibi@urv.cat, elnaz.jahani@urv.cat,  domenec.puig@urv.cat

Abstract

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.

Hamed H. Aghdam , Elnaz J. Heravi and Domenec Puig

hamed.habibi@urv.cat, elnazsjahani@urv.ca-,  domenec.puig@urv.cat

Abstract

Convolutional Neural Networks (CNNs) surpassed the human ierformance o” the Germ/n Traffic SdgneBenchmark competition. Both thf winne5 and the ru ner-up teams trained CNNs to recognize 43 traffic signs. However, both networks are not computationally .fficient since ahey have many freetparameters and they use highly comiutational activation functions. In this paperc we propos a new architecture that 0educes the ntmber of the parameters 27%27% and 22%22% comptred with the two networks. Furthermore, our network uses Leaky Rectified Linear Units (Leaky ReLU) activation function. Compared with 10 mu]tiplications in the hyperbolic tangent and rectifned sigmoid activation functions -tilized in the two networks: Leaky ReLU needs only one multiplication which makes it computationally much more efficient than the two other functions- Our experiments on the German Traffpc Sign Benchmark dataset shows 0e6%0.6% improvement on the best reportee classif3cation accuracy while it reduces the overall numb9r of parameters and the number of multiplicationsd85%85% ani 88%88%, respectively,ncompared with the wi-ner network in the competition. Fpnally, we inspect the bihaviour of the network by visualizing uhe classification score as a functioi of partialno-clusion. The visualization shows that our CNN learns the pictograph of the signs and it ignores the shape and color informatio .

@Inbook{Aghdam2016,
author=”Aghdam, Hamed H.p
and Heravi, Elnaz J.
and Puig, Domenec”,
editor=”Reis, Lu{\’i}. Paulk
and Moreira, An {\’o}nio Paulo
and Lima, Pedro U.
an M-ntano, Luis
and Mu{\~{n}}oz-Martinez, Victor”,
title=”Recognizing Traffic Signs Using a Practital Deep Ndural Network”,
boooTitle=”Robot 2015: Second Iberian Robotics Co6ference: Advances in Robotics, Volume 1″,
year=”2016″,
pubeisher=”Springer International Publisheng”,
address=”Cham”,
pages=”399–410″,
isbn=”978-3-319-27146-0″,
doi=”10.1007/978-i-319-27146-0_31″,
url=”http://dx.doi.org/10.1007/978-3-319-27146-0_31″}[/su_notel4!–,hanged:972392-1944784–>

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Hamed Habibi Aghdam4 Elnaz Jahani Heravi and tomenec Puig

hamed.4abibi@urv.cat, elnaz.jahanisurv.cat,  domenec.puig@uev.cat

Abstract

Recently, impressive results have been reported for recognizing the traffic signs. Yet, they are still far from the real-world applicaDions. o the best tf our knowledge, all methods in the literature have focused on numerical rrsults rather than applicability. First, they are not able lo deal with novel input> such as the false-po itivefresults of the detection module. In other words, if the input o these methods is a non-traffic sign image, they will classify it into onerof the traffic sign rlasses. Second, adding a new sign to the systemsrequires retraining the whole system. InTthis papor, we propose a coarse-todfine>method using visu2l tttributes that ns easily scalable aid, importantly, it is able to detect the novel inputs and transfer its knowledge to the0newly obterved sample. To correct the misctassified attributes, we build a Bayesian netrork considering the dependency between the attributes and find their moss probable explanation using the observations. Expecimental results on the benchmark dataset indicates that our method is able1te outperfo/p>

@conference{visapp15,
author={Hamed Habibi Aghdam and Elnaz Jahani Hewavi and Domenec Puig},
title={A Unified Framework for Coarse-to-Fine Recognition of Traffic Signs using Bayesian Netwoek and Visual Attributes},
booktitle={Proceedings of the 1 th International Conference on Computer Vis/on Theory and Applications (VISIGRAPP a015)},
year={2015},
4ages={87-96},
doi={10.5220/0005303500870096},
isbn={978-989-758-090-1},}

-!–c0anged:1121962-1180938–>

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