Memory‐Based Multiagent Coevolution Modeling for Robust Moving Object Tracking

Abstract

The three-stage human brain memory model is incorporated into a multiagent coevolutionary process for finding the best match of the appearance of an object, and a memory-based multiagent coevolution algorithm for robust tracking the moving objects is presented in this paper. Each agent can remember, retrieve, or forget the appearance of the object through its own memory system by its own experience. A number of such memory-based agents are randomly distributed nearby the located object region and then mapped onto a 2D lattice-like environment for predicting the new location of the object by their coevolutionary behaviors, such as competition, recombination, and migration. Experimental results show that the proposed method can deal with large appearance changes and heavy occlusions when tracking a moving object. It can locate the correct object after the appearance changed or the occlusion recovered and outperforms the traditional particle filter-based tracking methods.

Brain Strategy Algorithm for Multiple Object Tracking Based on Merging Semantic Attributes and Appearance Features

“…They divide the memory model into three stages: the sensory register, the short-term store, and the long-term store. Many researchers in computer vision have used this theory to introduce brain-memory-inspired MOT [18,19]. In [18], they established a memory model to handle the main challenges in MOT by using the human brain three-stage memory theory [12].…”

“…Their experimental results are promising for tackling sudden changes in appearance and occlusion. In [19], they are inspired by the same three-stage memory model. However, the three-stage memory model in this work aimed to find the right match between the appearance and the object by integrating the model into a multiagent coevolutionary process.…”

A Systematic Review of Coevolution in Real-Time Strategy Games

“…The mechanism of moving individuals between both short-term and long-term memory was managed later to emulate the psychology of human memory [6]. Moreover, some researchers considered a more precise model of the human brain to add another type of memory (Ultra Short-Term Memory) to the existing two [76].…”

Spinning tri-layer-circle memory modeling for template updating during moving object tracking

“…The proposed memorybased model can remember or forget what the scene has ever been which helps the model adapt to the variation of the scene much quickly. And we have applied the model to address two hot issues in computer vision: background modeling and object tracking, and obtained much better effects [18][19]. In this paper, on the basis of our previous work and learning from the spinning framework in [16], we aim to realize a computational memory model to imitate information storage and extraction by human brain.…”