Peipei Wu
Academic and research departments
Centre for Vision, Speech and Signal Processing (CVSSP), Department of Electrical and Electronic Engineering.ResearchResearch interests
My research interest is audio-visual tracking. Recent years, different sensors (such as microphones and cameras) have been used jointly to track multiple speakers in cluttered environments with a number of moving speakers and background noise. My work is to find a solution to augment the performance of audio-visual tracking.
Research interests
My research interest is audio-visual tracking. Recent years, different sensors (such as microphones and cameras) have been used jointly to track multiple speakers in cluttered environments with a number of moving speakers and background noise. My work is to find a solution to augment the performance of audio-visual tracking.
Publications
—Training a robust tracker of objects (such as vehicles and people) using audio and visual information often needs a large amount of labelled data, which is difficult to obtain as manual annotation is expensive and time-consuming. The natural synchronization of the audio and visual modalities enables the object tracker to be trained in a self-supervised manner. In this work, we propose to localize an audio source (i.e., speaker) using a teacher-student paradigm, where the visual network teaches the audio network by knowledge distillation to localize speakers. The introduction of multi-task learning, by training the audio network to perform source localization and semantic segmentation jointly, further improves the model performance. Experimental results show that the audio localization network can learn from visual information and achieve competitive tracking performance as compared to the baseline methods that are based on the audio-only measurements. The proposed method can provide more reliable measurements for tracking than the traditional sound source localization methods, and the generated audio features aid in visual tracking.
Intensity Particle Flow (IPF) SMC-PHD has been proposed recently for multi-target tracking. In this paper, we extend IPF-SMC-PHD filter to distributed setting, and develop a novel consensus method for fusing the estimates from individual sensors, based on Arithmetic Average (AA) fusion. Different from conventional AA method which may be degraded when unreliable estimates are presented, we develop a novel arithmetic consensus method to fuse estimates from each individual IPF-SMC-PHD filter with partial consensus. The proposed method contains a scheme for evaluating the reliability of the sensor nodes and preventing unreliable sensor information to be used in fusion and communication in sensor network, which help improve fusion accuracy and reduce sensor communication costs. Numerical simulations are performed to demonstrate the advantages of the proposed algorithm over the uncooperative IPF-SMC-PHD and distributed particle-PHD with AA fusion.