WHAT CAN MACHINE LEARNING BRING TO CROWD ANALYSIS, MODELLING AND SIMULATION? CONSIDERATIONS AND EXPERIMENTS GIUSEPPE VIZZARI CROWDS: MODELS AND CONTROL CIRM MARSEILLE, FRANCE, JUNE 3-7, 2019
OUTLINE • AI, Machine Learning (ML), a turbulent moment • A general schema for pedestrian and crowd research, and where can ML provide support • Sample applications of ML on pedestrian/crowd behaviour analysis • Sample applications of ML on pedestrian/crowd modelling and simulation • A look ahead
OUTLINE • AI, Machine Learning (ML), a turbulent moment • A general schema for pedestrian and crowd research, and where can ML provide support • Sample applications of ML on pedestrian/crowd behaviour analysis • Sample applications of ML on pedestrian/crowd modelling and simulation • A look ahead
AI, MACHINE LEARNING (ML), A TURBULENT MOMENT • I don’t have to tell you about the legitimate, sometimes excessive, and sometimes completely hyped interest around AI • Two main factors behind this return of Next Generation AI Development Plan, State Council attention, especially on machine learning of China 201 (attain AI supremacy by 2030) • Growth of computational power of devices (especially GPUs and even dedicated devices, e.g. TPUs) • Growing availability of data (especially pictures, videos, but also text in different languages) • This led to the development of novel open source machine learning frameworks • Legitimate research question: what can machine learning bring to crowd analysis, modelling and simulation?
OK, MACHINE LEARNING… BUT WHICH MACHINE LEARNING? Introducing Machine Learning
OK, MACHINE LEARNING… BUT WHICH MACHINE LEARNING? Introducing Machine Learning
OUTLINE • AI, Machine Learning (ML), a turbulent moment • A general schema for pedestrian and crowd research, and where can ML provide support • Sample applications of ML on pedestrian/crowd behaviour analysis • Sample applications of ML on pedestrian/crowd modelling and simulation • A look ahead
A GENERAL SCHEMA FOR PEDESTRIAN AND CROWD RESEARCH Synthesis Simulation campaign (i) Formalisation of execution phenomenologies Model and Simulation (ii) Metrics, indicators, simulator results techniques (iii) Generation of synthetic datasets Modelling Analysis of and design of results and a simulator interpretation (i) Motivations/goals for model innovation Target system Empirical data (ii) Data for calibration, Analysis of the validation, learning dynamics of target Analysis system
OUTLINE • AI, Machine Learning (ML), a turbulent moment • A general schema for pedestrian and crowd research, and where can ML provide support • Sample applications of ML on pedestrian/crowd behaviour analysis • A little state of the art • Clustering for lane identification and characterization • Sample applications of ML on pedestrian/crowd modelling and simulation • A look ahead
RELEVANT WORKS ON ML FOR AUTOMATED ANALYSIS OF PEDESTRIAN / CROWD BEHAVIOUR • Within the Computer Vision area lots of relevant work has been carried out on the border between surveillance and pedestrian/crowd studies • A particularly interesting case is represented by a work on PAMI on group detection GT!trajectories! • Francesco Solera, Simone Calderara, Rita Cucchiara: Socially Constrained Structural Learning for Groups Detection in Crowd. IEEE Trans. Pattern Anal. Mach. Intell. 38(5): 995-1008 (2016) • Authors came up wit a ML approach employing basic proxemic and group behavioural aspects that we developed in prior works… • …plus some annotated data we gathered
CLUSTERING FOR LANE IDENTIFICATION AND CHARACTERIZATION • Bi-directional flows are generally characterized by the formation of lanes • Few approaches proposed means of automated identification and quantitative characterization of this phenomenon • Order parameter [Rex & Loewen, 2007] • Clustering analysis [Hoogendoorn & Daamen, 2005] • Rotation measurement [Feliciani & Nishinari, 2016]
DBSCAN [ESTER, KRIEGEL, SANDER AND XU, 1996] • Unsupervised learning algorithm based on the concept of density • Parameters of the base version: 𝜁 , minPoints • Clusters determined through the concept of neighborhood: • if distance between 2 points is less than 𝜁 , they are neighbors • when one point has at least The choice of a suitable minPoints neighbors it is a core point distance metric is crucial, • a cluster is defined as the set of just as the values for neighboring core points, plus parameters neighboring points ( border points ) • Remaining points are considered noise
A TWO STEP DBSCAN APPROACH • We employed a two step clustering approach • The first application of DBCAN considers velocity vectors to separate main flows according to 1 . Velocity the direction vectors • The second one further subdivides clusters achieved from the previous step according also to positions • Different distance metrics, essentially evaluating in step (i) 2 . Positions & identified flow angle among velocities and in step directions (ii) distance among pedestrians (discounted for ) • Overall 4 parameters (different 𝜁 and minPoints in the two steps)
ACHIEVED RESULTS IN DIFFERENT EXPERIMENTS
AGREEMENT WITH HUMAN ANNOTATOR • Cohen’s Kappa coefficient is used to measure the level of inter-rater agreement between two coders in classifying a certain subject • Pedestrians have been classified considering: • their condition of belonging or not to any lane (i.e. gross classification – lane identification ) • their belonging to a certain lane (i.e. granular classification – lane characterization )
FURTHER WORK • Make the model more robust to changes in density • [SPOILER ALERT] Some ongoing work by Luca Crociani with Francesco Zanlungo to be discussed at TGF19 • Connect the dots: move from a frame by frame clustering to a time window, characterizing lanes in time • Intuition: the set of pedestrians in a lane can change, to a certain extent, without dissolving it or creating something new… • Jaccard similarity can properly represent this intuition • Additional validation elements are required • Could provide additional means of validation for models
OUTLINE • AI, Machine Learning (ML), a turbulent moment • A general schema for pedestrian and crowd research, and where can ML provide support • Sample applications of ML on pedestrian/crowd behaviour analysis • Sample applications of ML on pedestrian/crowd modelling and simulation • A little state of the art • Learning observables for multi-scale modelling and simulation • Deep neural networks for operational level behaviour modelling? • A look ahead
RELEVANT WORKS ON ML FOR SIMULATION OF PEDESTRIAN / CROWD BEHAVIOUR • Several past attempts of relatively little success • Works really worth attention • Reiforcement Learning approaches • Francisco Martinez-Gil, Miguel Lozano, Fernando Fernández: Emergent behaviors and scalability for multi-agent reinforcement learning-based pedestrian models. Simulation Modelling Practice and Theory 74: 117-133 (2017) • Generative Adversarial Networks approaches • Javad Amirian, Jean-Bernard Hayet, Julien Pettré: Social Ways: Learning Multi-Modal Distributions of Pedestrian Trajectories with GANs. CVPR Workshops 2019
LEARNING OBSERVABLES FOR MULTI- SCALE MODELLING AND SIMULATION • Allows simulating relatively large populations in urban areas • A multi-scale methodology is employed: • MATSim for road traffic • A CA -based microscopic model for pedestrian traffic • The environment is mainly represented by a network , but some parts can be simulated with higher fidelity using the CA, to better capture interaction among pedestrians • Iterative approach for the computation of routes… Crociani, L., Lämmel, G., & Vizzari, G. (2016). Simulation-aided crowd management: A multi-scale model for an urban case study. In International Workshop on Agent Based Modelling of Urban Systems (pp. 151-171)
MULTI-SCALE MODEL – ROUTE CHOICE • Sequential iterations of the simulation abstract this concept: Loop Lo p for # # iterat ations ns • 1 st iteration: shortest path • N th iteration: some agents re- compute their path, based on the MATSim MA Star St art experienced cost in the previous Routing Update iteration algorithm experienced • This allows the search of: travel times • Nash Equilibria , if the cost is the individual travel time of the agent • System Optimum , if the marginal cost of the population is considered Routes of agents • We basically add a more detailed simulation (shortest path at iteration the 1st iteration) account of some specific edge of the graph through a CA • But it comes at a cost!
THE ETT MODEL (1/5) • Goal : estimate the travel time of agents in the area of the environment associated to each link, given the surrounding conditions
THE ETT MODEL (2/5) • Goal : estimate the travel time of agents in the area of the environment associated to each link, given the surrounding conditions occupation
THE ETT MODEL (3/5) • Goal : estimate the travel time of agents in the area of the environment associated to each link, given the surrounding conditions
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