Audio‐Visual Speaker Tracking

Target motion tracking found its application in interdisciplinary fields, including but not limited to surveillance and security, forensic science, intelligent transportation system, driving assistance, monitoring prohibited area, medical science, robotics, action and expression recognition, individual speaker discrimination in multi‐speaker environments and video conferencing in the fields of computer vision and signal processing. Among these applications, speaker tracking in enclosed spaces has been gaining relevance due to the widespread advances of devices and technologies and the necessity for seamless solutions in real‐time tracking and localization of speakers. However, speaker tracking is a challenging task in real‐life scenarios as several distinctive issues influence the tracking process, such as occlusions and an unknown number of speakers. One approach to overcome these issues is to use multi‐modal information, as it conveys complementary information about the state of the speakers compared to single‐modal tracking. To use multi‐modal information, several approaches have been proposed which can be classified into two categories, namely deterministic and stochastic. This chapter aims at providing multimedia researchers with a state‐of‐the‐art overview of tracking methods, which are used for combining multiple modalities to accomplish various multimedia analysis tasks, classifying them into different categories and listing new and future trends in this field

Audio-Visual Speaker Tracking: Progress, Challenges, and Future Directions

Audio-visual speaker tracking has drawn increasing attention over the past few years due to its academic values and wide application. Audio and visual modalities can provide complementary information for localization and tracking. With audio and visual information, the Bayesian-based filter can solve the problem of data association, audio-visual fusion and track management. In this paper, we conduct a comprehensive overview of audio-visual speaker tracking. To our knowledge, this is the first extensive survey over the past five years. We introduce the family of Bayesian filters and summarize the methods for obtaining audio-visual measurements. In addition, the existing trackers and their performance on AV16.3 dataset are summarized. In the past few years, deep learning techniques have thrived, which also boosts the development of audio visual speaker tracking. The influence of deep learning techniques in terms of measurement extraction and state estimation is also discussed. At last, we discuss the connections between audio-visual speaker tracking and other areas such as speech separation and distributed speaker tracking

A multimodal approach to blind source separation of moving sources

A novel multimodal approach is proposed to solve the problem of blind source separation (BSS) of moving sources. The challenge of BSS for moving sources is that the mixing filters are time varying; thus, the unmixing filters should also be time varying, which are difficult to calculate in real time. In the proposed approach, the visual modality is utilized to facilitate the separation for both stationary and moving sources. The movement of the sources is detected by a 3-D tracker based on video cameras. Positions and velocities of the sources are obtained from the 3-D tracker based on a Markov Chain Monte Carlo particle filter (MCMC-PF), which results in high sampling efficiency. The full BSS solution is formed by integrating a frequency domain blind source separation algorithm and beamforming: if the sources are identified as stationary for a certain minimum period, a frequency domain BSS algorithm is implemented with an initialization derived from the positions of the source signals. Once the sources are moving, a beamforming algorithm which requires no prior statistical knowledge is used to perform real time speech enhancement and provide separation of the sources. Experimental results confirm that by utilizing the visual modality, the proposed algorithm not only improves the performance of the BSS algorithm and mitigates the permutation problem for stationary sources, but also provides a good BSS performance for moving sources in a low reverberant environment

3D AUDIO-VISUAL SPEAKER TRACKING WITH AN ADAPTIVE PARTICLE FILTER

reserved4siWe propose an audio-visual fusion algorithm for 3D speaker tracking from a localised multi-modal sensor platform composed of a camera and a small microphone array. After extracting audio-visual cues from individual modalities we fuse them adaptively using their reliability in a particle filter framework. The reliability of the audio signal is measured based on the maximum Global Coherence Field (GCF) peak value at each frame. The visual reliability is based on colour-histogram matching with detection results compared with a reference image in the RGB space. Experiments on the AV16.3 dataset show that the proposed adaptive audio-visual tracker outperforms both the individual modalities and a classical approach with fixed parameters in terms of tracking accuracy.Qian, Xinyuan; Brutti, Alessio; Omologo, Maurizio; Cavallaro, AndreaQian, Xinyuan; Brutti, Alessio; Omologo, Maurizio; Cavallaro, Andre

Object Tracking from Audio and Video data using Linear Prediction method

Microphone arrays and video surveillance by camera are widely used for detection and tracking of a moving speaker. In this project, object tracking was planned using multimodal fusion i.e., Audio-Visual perception. Source localisation can be done by GCC-PHAT, GCC-ML for time delay estimation delay estimation. These methods are based on spectral content of the speech signals that can be effected by noise and reverberation. Video tracking can be done using Kalman filter or Particle filter. Therefore Linear Prediction method is used for audio and video tracking. Linear prediction in source localisation use features related to excitation source information of speech which are less effected by noise. Hence by using this excitation source information, time delays are estimated and the results are compared with GCC PHAT method. The dataset obtained from [20] is used in video tracking a single moving object captured through stationary camera. Then for object detection, projection histogram is done followed by linear prediction for tracking and the corresponding results are compared with Kalman filter method

Audio-visual tracking of concurrent speakers

Audio-visual tracking of an unknown number of concurrent speakers in 3D is a challenging task, especially when sound and video are collected with a compact sensing platform. In this paper, we propose a tracker that builds on generative and discriminative audio-visual likelihood models formulated in a particle filtering framework. We localize multiple concurrent speakers with a de-emphasized acoustic map assisted by the image detection-derived 3D video observations. The 3D multimodal observations are either assigned to existing tracks for discriminative likelihood computation or used to initialize new tracks. The generative likelihoods rely on color distribution of the target and the de-emphasized acoustic map value. Experiments on AV16.3 and CAV3D datasets show that the proposed tracker outperforms the uni-modal trackers and the state-of-the-art approaches both in 3D and on the image plane