Object recognition in images is still one of the most important research topics in computer vision. Given an image or a video, the goal of object recognition is to recognize and localize all the objects.
In the last recent years, this topic has experienced an impressive gain in performance with the use of Deep Neural Networks1
and big datasets such as ImageNet 2. Despite of the research efforts, object recognition is an unsolved problem. For the methods that perform in real time (such as Deformable Part Models 3), the detection accuracy is low, while the methods that show higher performance can not run in real time. Actually, even the best current algorithms for object recognition are still far away from human performance. In this research line we focus on improving current systems, both in terms of accuracy and speed.
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J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei. Imagenet: A large-scale hierar- chical image database. In Proc. CVPR, 2009.
P. Felzenszwalb, R. Girshick, D. McAllester, D. Ramanan. Object Detection with Discriminatively Trained Part Based Models. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 32, No. 9, Sep. 2010.