| Input: image sequence, particle set |
| Output: particle set and the object state |
| Initialization: |
| (1) Initializing the tracked object manually. |
| (2) Constructing the particle set . |
| (3) Calculating the template . |
| Object tracking: |
| (1) for from 1 to the last frame do |
| (2) Estimate the object motion utilizing the proposed insect vision inspired detector and |
| obtain the response . |
| (3) Local search strategy: |
| (4) Propagating: According to the proposed state transition model (7), |
| propagate each particle state to get a new state for time . |
| (5) Re-sampling: Based on the state and the template, compute the weight of each |
| propagated particle at time , and then normalize the particles weight. |
| Generate a new particle set . |
| (6) Lost detection: (to address severe occlusion and drifts problems) |
| (7) Count the number of valid samples and test the Two-Prerequisites. If the Two-Prerequisites are satisfied, |
| let LF = 1, otherwise, LF = 0. (LF is the label to mark the lost.) |
| (8) Global search strategy: |
| (9) if LF = 1 (the tracker loses the object) or for every frames then |
| (10) Refining: Search the interest image region where to seek for a more accurate state . |
| (11) Weighting: Based on the state and the template, compute the weight of each particle at time |
| and normalize it. |
| (12) end if |
| (13) Estimating: |
| (14) Calculate the object position at time and get the best state: , where . |
| (15) Online template update: |
| (16) Update the template according to (15) for every frames. |
| (17) end for |
