Crowd tracking with box particle filtering

This paper focuses on tracking large groups of objects, such as crowds of pedestrians. Large groups generate multiple measurements with uncertain origin. Additionally, often the sensor noise characteristics are unknown but bounded within known intervals. Hence, these two types of uncertainties call for flexible techniques capable of offering a solution in the presence of data association and also to cope with the presence of nonlinearities. This paper presents a box particle filter for large crowds tracking able to deal with such challenges. The filter measurement update step is performed by solving a dynamic constraint satisfaction problem (DSCP) with the multiple measurements. The box particle filter performance is validated over a realistic scenario comprising a large crowd of pedestrians. Promising results are presented in terms of accuracy and computational complexity.