Object tracking & Pedestrian detection

 In this project, we are using Kalman and Particle filters to track motions of objects. (hands, pedestrians)

With naive Kalman filter implementation, it is easy to track a moving pedestrian without major shape, or shadow change.

Kalman filter (KF) tracking a pedestrian. Blue circle represent the distribution of the KF gaussian

With particle filter, we track the face of Romney. The green frames enclose the target window, and blue points represent particles in particle filter (PF). The following frames are with different noise levels, but all get good face detections.

As for tracking Romney’s left hand, things are not as trivial since the appearance of his hand keeps changing. Thus we take history into account and update the tracking window model continuously, following:

Template(t)=αBest(t)+(1-α)Template(t-1)

Hand tracking with history updating

Till now, we did not consider the change of size in tracking a target. In order to consider size, we need to let the particles in PF have different window sizes.

Tracking pedestrian with changing size

Other applications are also considered, such as tracking different targets at the same time.

Tracking three pedestrians

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