1. Date of download: 6/1/2016 Copyright © 2016 SPIE. All rights reserved. Diagram of proposed three-step iterative video target tracking algorithm. Figure Legend: From: Interval recursive least-squares filtering with applications to video target tracking Opt. Eng. 2008;47(10):106401-106401-14. doi:10.1117/1.2993320

2. Date of download: 6/1/2016 Copyright © 2016 SPIE. All rights reserved. Target appearance changes, occlusion, and disturbance from multiple moving objects in three video sequences: (a) the first frame in the first sequence; (b) the 100th frame in the first sequence; (c) the 10th frame in the second sequence; (d) the 89th frame in the second sequence; (e) the 41st frame in the second sequence; (f) the 68th frame in the second sequence; (g) the third frame in the third sequence; (h) the 60th frame in the third sequence. Figure Legend: From: Interval recursive least-squares filtering with applications to video target tracking Opt. Eng. 2008;47(10):106401-106401-14. doi:10.1117/1.2993320

3. Date of download: 6/1/2016 Copyright © 2016 SPIE. All rights reserved. Overlap rates for tracking a car using the RLS filter and the interval RLS filter in the first sequence: (a) λ=0.9; (b) λ=0.8; (c) λ=0.7; (d) λ=0.3; (e) λ=0.2; (f) λ=0.1. Figure Legend: From: Interval recursive least-squares filtering with applications to video target tracking Opt. Eng. 2008;47(10):106401-106401-14. doi:10.1117/1.2993320

4. Date of download: 6/1/2016 Copyright © 2016 SPIE. All rights reserved. Overlap rates for tracking a car using the RLS filter and the interval RLS filter in the second sequence: (a) λ=0.9; (b) λ=0.8; (c) λ=0.7; (d) λ=0.3; (e) λ=0.2; (f) λ=0.1. Figure Legend: From: Interval recursive least-squares filtering with applications to video target tracking Opt. Eng. 2008;47(10):106401-106401-14. doi:10.1117/1.2993320

5. Date of download: 6/1/2016 Copyright © 2016 SPIE. All rights reserved. Size of search region for tracking a car and a person using the interval RLS filter with λ=0.9: (a) for tracking a car in the first sequence, (b) for tracking a car in the second sequence, (c) for tracking a person in the third sequence. Figure Legend: From: Interval recursive least-squares filtering with applications to video target tracking Opt. Eng. 2008;47(10):106401-106401-14. doi:10.1117/1.2993320

6. Date of download: 6/1/2016 Copyright © 2016 SPIE. All rights reserved. Results of tracking state Xk(1,1) using the RLS filter with λ=0.9 and the Kalman filter. Figure Legend: From: Interval recursive least-squares filtering with applications to video target tracking Opt. Eng. 2008;47(10):106401-106401-14. doi:10.1117/1.2993320

7. Date of download: 6/1/2016 Copyright © 2016 SPIE. All rights reserved. Comparison of the tracking errors between the RLS filter with λ=0.9 and the Kalman filter and the tracking errors of the RLS filter with sets of larger and smaller λ values: (a) the RLS filter with λ=0.9 and the Kalman filter; (b) the RLS filter with λ=0.9; (c) the RLS filter with λ=0.85; (d) the RLS filter with λ=0.8; (e) the RLS filter with λ=0.75; (f) the RLS filter with λ=0.25; (g) the RLS filter with λ=0.2; (h) the RLS filter with λ=0.15; (i) the RLS filter with λ=0.1. Figure Legend: From: Interval recursive least-squares filtering with applications to video target tracking Opt. Eng. 2008;47(10):106401-106401-14. doi:10.1117/1.2993320

8. Date of download: 6/1/2016 Copyright © 2016 SPIE. All rights reserved. Results of tracking state Xk(1,1) using the interval Kalman filter and the interval RLS filter with larger and smaller λ values: (a) the interval Kalman filter; (b) the interval RLS filter with λ=0.9; (c) the interval RLS filter with λ=0.85; (d) the interval RLS filter with λ=0.8; (e) the interval RLS filter with λ=0.75; (f) the interval RLS filter with λ=0.25; (g) the interval RLS filter with λ=0.2; (h) the interval RLS filter with λ=0.15; (i) the interval RLS filter with λ=0.1. Figure Legend: From: Interval recursive least-squares filtering with applications to video target tracking Opt. Eng. 2008;47(10):106401-106401-14. doi:10.1117/1.2993320

9. Date of download: 6/1/2016 Copyright © 2016 SPIE. All rights reserved. Performance comparison for tracking a car and a person using the RLS and interval RLS filters with λ=0.9 in one frame of the respective sequences: (a) tracking a car at the 36th frame in the first sequence using the RLS filter; (b) tracking a car at the 67th frame in the second sequence using the RLS filter; (c) tracking a person at the 71st frame in the third sequence using the RLS filter; (d) tracking a car at the 36th frame in the first sequence using the interval RLS filter; (e) tracking a car at the 67th frame in the second sequence using the interval RLS filter; (f) tracking a person at the 71st frame in the third sequence using the interval RLS filter. Figure Legend: From: Interval recursive least-squares filtering with applications to video target tracking Opt. Eng. 2008;47(10):106401-106401-14. doi:10.1117/1.2993320

10. Date of download: 6/1/2016 Copyright © 2016 SPIE. All rights reserved. Overlap rates for tracking a person using the RLS filter and the interval RLS filter in the third sequence: (a) λ=0.9; (b) λ=0.8; (c) λ=0.7; (d) λ=0.3; (e) λ=0.2; (f) λ=0.1. Figure Legend: From: Interval recursive least-squares filtering with applications to video target tracking Opt. Eng. 2008;47(10):106401-106401-14. doi:10.1117/1.2993320