Hamed Habibi Aghdam
Elnaz Jahani Heravi
University Rovira i Virgili, Spain
Rovira i Virgili University, Spain
Traffic Sign Recognition, Visual Attributes, Bayesian Network, Most Probable Explanation, Sparse Coding.
Recently, impressive results have been reported for recognizing the traffic signs. Yet, they are still far from the real-world applications. To the best of our knowledge, all methods in the literature have focused on numerical results rather than applicability. First, they are not able to deal with novel inputs such as the false-positive results of the detection module. In other words, if the input of these methods is a non-traffic sign image, they will classify it into one of the traffic sign classes. Second, adding a new sign to the system requires retraining the whole system. In this paper, we propose a coarse-to-fine method using visual attributes that is easily scalable and, importantly, it is able to detect the novel inputs and transfer its knowledge to the newly observed sample. To correct the misclassified attributes, we build a Bayesian network considering the dependency between the attributes and find their most probable explanation using the observations. Experimental resul
ts on the benchmark dataset indicates that our method is able to outperform the state-of-art methods and it also possesses three important properties of novelty detection, scalability and providing semantic information.