A multi-agent dynamic system for robust multi-face tracking

Abstract

The paper presents a new architecture framework in the field of expert and intelligent systems which is based on four paradigms: a novel multi-agent dynamic system architecture (MADS), an extended Belief Desire Intention (EBDI) agent community, autonomy-oriented entities (AOEs), and deep learning concepts. The main impact of the proposed framework is a new approach, or even a new way of thinking, which enables integration of the concepts of deep learning, a conventional approach to solving the domain problem, cognitive agents with mental attitudes, and concepts of nature-inspired computing. All these allow the effective use of the framework in the field of intelligent and expert systems. The significance of the framework lies in its flexibility and adaptability based on the formal logical description of EBDI agents, the definition of the behaviour of AOEs, the use of classical modules for domain problem solving, and modules based on deep-learning concepts. We believe that the example of the adaptation of the proposed architecture framework to robust multi-face tracking illustrates the significance of the proposed framework. In this paper, MADS is adapted to the first two stages of a face de-identification pipeline: robust face detection and multi-face tracking. The proposed architecture of MADS has a two-level hierarchical organization. At the first level there is a manager designed as an Extended Belief Desire Intention (mEBDI) agent. The extension of a manager BDI agent consists of a convolutional neural network-based face detector, a set of autonomous-oriented entities for the elimination of false positive face detections, and a trajectory memory. At the second level, there are many tracking agents (trEBDIs) which consist of a basic BDI agent extended with a face tracker based on position and scale correlation filters, a visual appearance memory, and a trajectory memory. The mEBDI and trEBDI agents are defined by the modal logic and are described at the implementation level. The proposed architecture for a robust multi-face tracking system was tested on a subset of YouTube music videos. The qualitative results, as well as the preliminary quantitative results expressed by the standard testing metrics, demonstrate the effective adaptation of the proposed multi-agent dynamic architecture to a robust multi-face tracking system.