Object detection is a fundamental ability for robots interacting within an environment. While stunningly effective, state-of-the-art deep learning methods require huge amounts of labeled images and hours of training which does not favour such scenarios. This work presents a novel pipeline resulting from integrating (Maiettini et al. in 2017 IEEE-RAS 17th international conference on humanoid robotics (Humanoids), 2017) and (Maiettini et al. in 2018 IEEE/RSJ international conference on intelligent robots and systems (IROS), 2018), which naturally trains a robot to detect novel objects in few seconds. Moreover, we report on an extended empirical evaluation of the learning method, justifying that the proposed hybrid architecture is key in leveraging powerful deep representations while maintaining fast training time of large scale Kernel methods. We validate our approach on the Pascal VOC benchmark (Everingham et al. in Int J Comput Vis 88(2): 303–338, 2010), and on a challenging robotic scenario (iCubWorld Transformations (Pasquale et al. in Rob Auton Syst 112:260–281, 2019). We address real world use-cases and show how to tune the method for different speed/accuracy trades-off. Lastly, we discuss limitations and directions for future development.
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|Titolo:||On-line object detection: a robotics challenge|
|Data di pubblicazione:||2020|
|Appare nelle tipologie:||01.01 - Articolo su rivista|