This paper compares particle swarm optimization and a genetic algorithm for perception by a partner robot. The robot requires visual perception to interact with human beings. It should basically extract moving objects using visual perception in interaction with human beings. To reduce computational cost and time consumption, we used differential extraction. We propose human head tracking for a partner robot using particle swarm optimization and a genetic algorithm. Experiments involving two maximum iteration numbers show that particle swarm optimization is more effective in solving this problem than genetic algorithm

Kubota, Naoyuki

Sulistijono, Indra Adji

English

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Human Head Tracking Based on PSO and GAPaper:Human Head Tracking Based on Particle Swarm Optimizationand Genetic AlgorithmIndra Adji Sulistijono∗,∗∗ and Naoyuki Kubota∗∗∗,∗∗∗∗∗Dept. of Mechanical Engineering, Graduate School of Engineering, Tokyo Metropolitan University6-6 Asahigaoka, Hino, Tokyo 191-0065, Japan∗∗Electronics Eng. Polytechnic Institute of Surabaya - ITS (EEPIS-ITS), Kampus ITS Sukolilo, Surabaya 60111, IndonesiaE-mail: indra-adji@ed.tmu.ac.jp∗∗∗Dept. of System Design, Tokyo Metropolitan University6-6 Asahigaoka, Hino, Tokyo 191-0065, Japan∗∗∗∗SORST, Japan Science and Technology Agency (JST)E-mail: kubota@comp.metro-u.ac.jp[Received January 17, 2007; accepted March 20, 2007]This paper compares particle swarm optimization anda genetic algorithm for perception by a partner robot.The robot requires visual perception to interact withhuman beings. It should basically extract moving ob-jects using visual perception in interaction with hu-man beings. To reduce computational cost and timeconsumption, we used differential extraction. We pro-pose human head tracking for a partner robot usingparticle swarm optimization and a genetic algorithm.Experiments involving two maximum iteration num-bers show that particle swarm optimization is more ef-fective in solving this problem than genetic algorithm.Keywords: human head tracking, particle swarm opti-mization, steady-state genetic algorithm, visual percep-tion, partner robot1. IntroductionVisual perception is important for the partner robots,because it may provide information useful in interactionwith human partners. Partner robots entertaining, assist-ing or otherwise interacting with human users should beable to recognize human beings, interact with them us-ingnaturalcommunication, andlearnhowhumanpartnersmight want to interact.Most human detection involves detecting single humanor face images, assuming that human beings are local-ized in individual frames based on human models – shape,contrast, and color. Robots could determine human inten-tions using built in maps, because tasks depend on envi-ronmental conditions. Beyond environmental map learn-ing, robots should determine human gestures and posturesin communicating with users. Previous studies have pro-posed using image processing for robotic visual percep-tion such as differential ﬁlters, moving object detection,and pattern recognition [16,17]. For pattern recognition,robots require patterns or templates, but a human-friendlyrobot cannot know patterns or templates beforehand sothey must learn patterns and templates while interactingwith users. Previous research has had a robot detect ahuman using self-organizing map (SOM) based on colorpatterns and location of a human obtained from the SOMnodes [25,26].We made a multi purpose partner robot, one purposebeing to communicate with a user. To reach this goal, therobot’s visual perception focuses on face recognition andhuman head tracking, i.e., head location and extraction.Particle swarm optimization (PSO) and genetic algo-rithm (GA) are two widely used types of evolutionarycomputation (EC). As are other EC techniques, PSO is apopulation-based search algorithm and is initialized witha population of random solutions, called particles. Unlikethe other evolutionary computation techniques, each PSOparticle is associated with a velocity. Particles ﬂy throughthe search space with velocities dynamically adjustedbased on their historical behaviors. Particles tend to ﬂy to-ward increasingly better search areas during a search [18].Many research results have been reported [11,12,20–22].GAs are adaptive optimization techniques that simulatethe mechanics of genetic evolution and have been appliedto a wide variety of problems [13,14]. We propose us-ing PSO or GA in head tracking, to reduce computationalcost. The human search problem is formulated into a non-linear integer optimization problem. Integer variables areparameters that represent a subwindow in an image as atemplate. The objective function involves maximizing theﬁtness function. First, the robot extracts a human from animage taken by a built-in charge-coupled device (CCD)camera. Assuming that human beings move, moving ob-jects become candidates for human beings. A differentialextraction area is formed to reduce time cost consumptionand the search area. The robot searches for head using ahuman head template. Our results showed that PSO per-forms better in some cases while GAs perform better inothers.Thispaperisorganizedasfollows. Section2introducesa vision-based partner robot, and proposes human headVol.11 No.6, 2007 Journal of Advanced Computational Intelligence 681and Intelligent InformaticsSulistijono, I. A. and Kubota, N.Pan-Tilt CCD CameraWireless Keyboard & MouseDC MotorUltrasonic Sensor PC UnitControl UnitWireless LANFig. 1. MOBiMac partner robot.Rotary EncoderDC MotorCCD CameraHost ComputerPWM BoardUPP BoardSensor BoardUltra-sonicSensor 8chV25 CPU RS 232CRotary EncoderDC MotorCCD CameraHost ComputerPWM BoardUPP BoardSensor BoardUltra-sonicSensor 8chV25 CPU RS 232CFig. 2. MOBiMac control architecture.tracking based on visual perception. Section 3 details ex-perimental results and Section 4 presents conclusions.2. Visual Perception2.1. Partner RobotWe developed a MOBiMac partner robot (Fig. 1)t ob eused as a personal computer and partner. One of twoCPUs is used for PC and the other for robotic behavior.The robot has two servomotors, four ultrasonic sensors,and a CCD camera (Fig. 2). The ultrasonic sensor mea-sures 2000 mm, so the robot captures behavior such ascollision avoidance, human approach, and visual tracking.The robot uses CCD camera to extract a human beingsand, it detects them, extracts color patterns in visual track-ing (Fig. 3), detailed below.Satisfy?YNImage captureDifferential extractionFound the windowPSO /GAUpdating newpopulationFitness functionInitialization ofa populationFig. 3. Visual tracking architecture.2.2. Differential Extraction for Attention RangeUsing two continuous images to detect moving objectsand set the attention range reduces computational costsand improves the detection accuracy in images with com-plex backgrounds.We use differential extraction to form the attentionrange. Because image processing uses much time andcomputational cost, using full-sized of image processingis impractical, so we use reduced images. First, the robotcalculates the center of gravity of pixels differing fromthe previous image in differential extraction, using 40 ×30 pixel images. Once a human being is detected, the at-tention range is formed based on the center of gravity ofthe pixel of the moving object for horizontal and verticalaxes, deﬁning the highest pixel detected as the top range.Using RGB color space image taken by CCD camera,we extract colors corresponding to hair and face usingthresholds.2.3. Particle Swarm OptimizationPSO, developed by Kennedy and Eberhart in 1995, wasinspired by the social behavior of a ﬂock of migratingbirds trying to reach an unknown destination. In PSO,each solution is a bird in the ﬂock, referred to as a parti-cle. Birds in the population evolve in social behavior andmovement toward a destination [11].Physically, this mimics a ﬂock of birds that mutuallycommunicate as they ﬂy. Each bird looks in a speciﬁcdirection, and when communicating identify the bird inthe best location. Each bird proceeds toward the best birdusing a velocity depending on its current position. Eachbird then, studies the search space from its new local po-sition, and the process is repeated until the ﬂock reaches adesired destination. Note that this involves both social in-teraction and intelligence, meaning that birds learn fromexperience (local search) and from the experience of oth-ers around them (global search).682 Journal of Advanced Computational Intelligence Vol.11 No.6, 2007and Intelligent Informatics

Human Head Tracking Based on Particle Swarm Optimization and Genetic Algorithm

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