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This work implements a moving object tracking algorithm based on the particle filter approach, more precisely, a kernel tracking algorithm based on the particle filter approach.
Components:
- Appearance model
- Motion model
The particle filter method is a good tracking method because it is robust to background noise and partial occlusion of the tracked objects.
The color histogram allows to give more information about the object but also improves state tracking when used with particle filters.
Weighting allows to reduce the number of particles needed for tracking without affecting the precision, quite the contrary.
On the other hand, it is necessary to have a sufficiently large number of particles for the precision to be good, which entails a cost in terms of computation time.
[1] ResearchGate website, Article "Direction Based Modified Particle Filter for Vehicle Tracking" published in 2015
[2] ResearchGate website, Article "Assessment of Vision-Based Vehicle Tracking for Traffic Monitoring Applications" , author: Dale Joshua R. Del Carmen et Rhandley Cajote published in 2018.