A Distributed Lifelong System for the Distributed Multi-Agent Multi-Object Tracking Challenge Fabio Previtali 15th October 2013 Rome, Italy
Outline Problem statement 1 Multi-Clustered Particle Filtering 2 Experimental evaluation 3 BeeSafe project 4 Research plan and future directions 5 A Distributed Lifelong System for the Distributed MAMOT Challenge 15th October 2013 2 / 21
Multi-Object Detection and Tracking A brief overview Moving Object Detection real-time extraction of moving objects from sensors Object Tracking [5] continuous observation of the objects over time to form persistent trajectories of the objects 5. Yilmaz et al., “Object tracking: A survey” in Journal ACM Computing Surveys (CSUR), 2006 A Distributed Lifelong System for the Distributed MAMOT Challenge 15th October 2013 3 / 21
A distributed lifelong system for the Distributed Multi-Agent Multi-Object Tracking challenge + Ultimate Goal Creating a distributed lifelong system able to continuously track the objects in an environment using multiple sensors and that is able to learn the behaviors of the tracked objects A Distributed Lifelong System for the Distributed MAMOT Challenge 15th October 2013 4 / 21
Related work Works that have significantly contributed to the improvement of one or more aspects of multiple target tracking. a synchronous algorithm based on the exchange of Gaussian Mixture Models parameters that are evaluated using a distributed Expectation-Maximization method [1] a distributed approach that clusterizes the particle cloud in order to detect the targets’ poses [4] an asynchronous approach where the posterior probability is evaluated using a gossiping protocol for data dissemination [2] 1. Gu et al., “Distributed particle filter for target tracking” in ICRA 2007 2. Oreshkin et al., “Asynchronous distributed particle filter via decentralized evaluation of Gaussian products” in Information Fusion (FUSION), 2010 4. Wu et al., “Boosted Interactively Distributed Particle Filter for automatic multi-object tracking” in 15th IEEE International Conference on Image Processing, 2008 A Distributed Lifelong System for the Distributed MAMOT Challenge 15th October 2013 5 / 21
Multi-Clustered Particle Filtering A Distributed Multi-Agent Multi-Object Tracking method based on a Multi-Clustered Particle Filter . Input: a set of positions of the objects provided by a Multi-Object detection system Output: the estimated trajectories of the moving objects over time It is not a general solution yet... � no target identity A Distributed Lifelong System for the Distributed MAMOT Challenge 15th October 2013 6 / 21
Multi-Clustered Particle Filtering Novelty The novelties of the approach are: a new clustering technique that keeps track of a variable unknown number of objects an improved robustness and reduced network overload by using Gaussian Mixture Models an asynchronous approach to improve the flexibility and the robustness of the entire system A Distributed Lifelong System for the Distributed MAMOT Challenge 15th October 2013 7 / 21
Multi-Clustered Particle Filtering Estimation Process Agent a ̃ a , μ t a , σ t a ) a ξ t a (λ t s z t r o KClusterize s Particle Filter n e S 1 , μ t 1 , σ t 1 ) (λ t k r o w t a − 1 , μ t a − 1 , σ t a − 1 ) (λ t ̃ ' e A ξ t N Distributed KClusterize Particle Filter a + 1 , μ t a + 1 , σ t a + 1 ) (λ t a , Μ t a , Σ t a ) (Λ t ' , μ t ' , σ t ' ) (λ t A A A A Distributed Lifelong System for the Distributed MAMOT Challenge 15th October 2013 8 / 21
Experimental evaluation Application field RoboCup Standard Platform League using the Aldebaran Nao humanoid robots and considering the task of tracking the ball in the soccer field. The Multi-Clustered Particle Filter method is the core of the PTracking software library used for the experimental evaluation. A Distributed Lifelong System for the Distributed MAMOT Challenge 15th October 2013 9 / 21
Experimental evaluation Objective Demonstrate, using SimRobot [3] simulator, the robustness of the PTracking software library by adding: robot localization noise perception noise ◮ Increasing the duration of false perceptions ◮ Increasing the number of false perceptions We compared the performance, in terms of robustness, of our method with respect to the algorithm developed by Wu et al. [4]. 3. R¨ ofer et al.: “B-human team report and code release 2011”. Technical report, 2011 4. Wu et al., “Boosted Interactively Distributed Particle Filter for automatic multi-object tracking” in 15th IEEE International Conference on Image Processing, 2008 A Distributed Lifelong System for the Distributed MAMOT Challenge 15th October 2013 10 / 21
Experimental results Nominal situation We just introduced the robot localization noise. A Distributed Lifelong System for the Distributed MAMOT Challenge 15th October 2013 11 / 21
Experimental results Increasing the duration of false perceptions We introduced both the robot localization noise and we increased the duration of false perceptions. A Distributed Lifelong System for the Distributed MAMOT Challenge 15th October 2013 12 / 21
Experimental results Increasing the number of false perceptions We introduced both the robot localization noise and we increased the number of false perceptions. A Distributed Lifelong System for the Distributed MAMOT Challenge 15th October 2013 13 / 21
BeeSafe project I am also working on the BeeSAFE project about “ Learning for Multi-Robot Task Allocation ” for the Sistemi Software Integrati (SSI) company. Objective Developing a system that is able to autonomously coordinate a team of robots using learning techniques The project involves two main activities: SLAM Multi-Robot 1 Multi-Robot Task Allocation 2 A Distributed Lifelong System for the Distributed MAMOT Challenge 15th October 2013 14 / 21
Preliminary results We successfully developed a system to learn the best path for a robot, from a point A to a point B, in a simulative environment. Learning applied on the The high level actions defined using the choices points. Petri Net Plans formalism. A Distributed Lifelong System for the Distributed MAMOT Challenge 15th October 2013 15 / 21
Learning best combined navigational abilities The experimental objective is to demonstrate that: an optimal solution for a single robot can be different when 1 introducing multiple robots implicit coordination among the robots in order to reach the best 2 combined solution A best combined path for two robots in the same environment. A Distributed Lifelong System for the Distributed MAMOT Challenge 15th October 2013 16 / 21
Research plan My research plan for the next year will focus on: extending the designed method 1 ◮ implementing the ability of determining also the identities of the tracked objects; ◮ introducing a component for data association. starting to learn basic behaviors of the tracked objects 2 A Distributed Lifelong System for the Distributed MAMOT Challenge 15th October 2013 17 / 21
Future directions Tracking of individuals within a crowd using the PETS2009 dataset Ground-truth Acquisition of Humanoid Soccer Robot Behavior A Distributed Lifelong System for the Distributed MAMOT Challenge 15th October 2013 18 / 21
Ingegneria degli Algoritmi (type A): 30/30 CITEC Ph.D. Summer School (type B): seminar on 17th October with Prof. Iocchi to get a mark Website of my seminars: www.cs.rug.nl/ds/Seminars/Program A Distributed Lifelong System for the Distributed MAMOT Challenge 15th October 2013 19 / 21
Publications “ Distributed Multi-Agent Multi-Object Tracking with a Multi-Clustered Particle Filtering ” submitted to the 13th International Conference on Autonomous Agents and Multiagent Systems ( AAMAS 2014 ) A Distributed Lifelong System for the Distributed MAMOT Challenge 15th October 2013 20 / 21
Activities RoboCup 2013 , Eindhoven , The Netherlands RoboCup German Open 2013 , Magdeburg , Germany . Third place RoboCup Iran Open , Tehran , Iran . First place A Distributed Lifelong System for the Distributed MAMOT Challenge 15th October 2013 21 / 21
Dongbing Gu. Distributed particle filter for target tracking. In IEEE International Conference on Robotics and Automation, 2007 , pages 3856–3861, 2007. Boris N Oreshkin and Mark J Coates. Asynchronous distributed particle filter via decentralized evaluation of gaussian products. In 13th Conference on Information Fusion (FUSION), 2010 , pages 1–8, 2010. Thomas R¨ ofer et al. B-human team report and code release 2011. Technical report, available online: www.b-human.de/downloads/bhuman11_coderelease.pdf . Yi Wu, Xiaofeng Tong, Yimin Zhang, and Hanging Lu. Boosted interactively distributed particle filter for automatic multi-object tracking. In 15th IEEE International Conference on Image Processing, 2008. ICIP 2008 , pages 1844–1847, 2008. Alper Yilmaz, Omar Javed, and Mubarak Shah. Object tracking: A survey. Acm Computing Surveys (CSUR) , 38(4):13, 2006. A Distributed Lifelong System for the Distributed MAMOT Challenge 15th October 2013 21 / 21
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