Off-line multiple object tracking using candidate selection and the viterbi algorithm - KOKARAM, ANIL; PITIE, FRANCOIS (My University, 2005)
This paper presents a probabilistic framework for off-line multiple object tracking. At each timestep, a small set of deterministic candidates is generated which is guaranteed to contain the correct solution. Tracking an object within video then becomes possible using the Viterbi algorithm. In contrast with particle filter methods where candidates are numerous and random, the proposed algorithm involves a few candidates and results in a deterministic solution. Moreover, we consider here off-line applications where past and future information is exploited. This paper shows that, although basic and very simple, this candidate selection allows the solution of many tracking problems in different real-world applications and offers a good alternative to particle filter methods for off-line applications.
IEEE International Conference on Image Processing (ICIP'05)
PUBLISHED
Genoa, Italy
Keywords: maximum likelihood estimation; object detection