A Corpus-Guided Framework for Robotic Visual Perception

Ching Lik Teo, Yezhou Yang, Hal Daumé III, Cornelia Fermüller, Yiannis Aloimonos

Abstract: We present a framework that produces sentence-level summarizations of videos containing complex human activities that can be implemented as part of the Robot Perception Control Unit (RPCU). This is done via: 1) detection of pertinent objects in the scene: tools and direct-objects, 2) predicting actions guided by a large lexical corpus and 3) generating the most likely sentence description of the video given the detections. We pursue an active object detection approach by focusing on regions of high optical flow. Next, an iterative EM strategy, guided by language, is used to predict the possible actions. Finally, we model the sentence generation process as a HMM optimization problem, combining visual detections and a trained language model to produce a readable description of the video. Experimental results validate our approach and we discuss the implications of our approach to the RPCU in future applications.