Crowd Self-Organization, Streaming and Short Path Smoothing 學號： 姓名：邱欣怡 日期： 2007/1/2 Stylianou Soteris & Chrysanthou Yiorgos

Outline  Introduction  Related work  Method & Algorithm  Result  Performance  Conclusion

Introduction  Collective behavior of pedestrians, known as self-organization  Flow grid mechanism in introduced to simplify navigation and enable crowd self organization

Related Work  Crowd navigation Deal with the problem of steering an avatar inside a large amount of static and moving obstacles

Related Work (conti.)  Approaches to solving the navigation problem 1. Path planning method Not good for dense crowd navigation 2. Reactive navigation method Significant work 3. Behavioral navigation method Simulate aspects of pedestrian behavior

Related Work (conti.)  Reactive navigation method 1. Force field method Collision avoidance and smooth steering 2. Rule based method Model complex behaviors through a combination of attributes and rule, and through FSM 3. XZT space method Using space-time represent movements of dynamics objects

Methodology  First,flow grid is constructed over the walk area Flow grid->measure densities and velocities at various directions  Using flow grid to navigate  Using Steering algorithm to local steering and smoothing

Measuring the Flows Each avatar is registered on the grid by distributing his density and velocity to the 4 neighboring points Velocities are separated into X and Z axis components and stored at each point, (+vx,-vx,+vz,-vz)

Complete Picture

Using the Flow Grid to Navigate  Weight = (1+D) * (1+AngleDiff(T,F)) D = density at spot T = vector showing direction towards target pos F = vector showing direction of flow at spot AngleDiff =Angle difference between 2 vectors in radians

Steering Algorithm  Using discrete occupancy map Actual avatar First 4 position are used for curve interpolation (Catmul- Rom curve) Last 2 position are used for path smoothing (in order to minimize the turn angle)

Next position search Search starts from the top and checks left and right at an increasing angle and reducing distance until an empty cell found

Result Two Parallel But Opposing Streams

Result Two Crossing Crowd Streams

Result Resulting paths of parallel but opposing streams Resulting paths of perpendicularly crossing streams

Performance Improved Density of avatars. Approximate crowd jam limits for different area sizes

Performance Local Navigation performance cost

Conclusion  Flow grid gives an overall impression of what kind of pedestrian traffic exists in that area, and it can detect Congested araes.  Steering algorithm can reduce the collisions avoidance cost.