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1 Integration of Background Modeling and Object Tracking Yu-Ting Chen, Chu-Song Chen, Yi-Ping Hung IEEE ICME, 2006

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2 Outline Introduction General BG Modeling Description Variable Threshold Selection Experiment results Conclusion

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3 Introduction (1/4) Background modeling: Important for many applications: Visual surveillance. Human gesture analysis. Moving object detection: BG and FG classification. Method: Mixture of Gaussian distribution (in this paper) Pixel-wise. Appropriate to dynamic BG.

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4 Introduction (2/4) Object tracking: Appearance model: Color histogram is used (in this paper). Measure the similarity of the target object and candidates. Search algorithm: Find the most likely state of tracked object via similarity measurement. Particle filtering is used (in this paper).

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5 Introduction (3/4) The key to classify FG and BG: Threshold: T In previous research: a static T was applied However, T should be adapted according to: Color distance between BG and object: Large => loose T Small => strict T

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6 Introduction (4/4) Generally, BG modeling and object tracking are independent. While in this paper: Object trackingBG modeling Find discriminative T Get robust tracking result Use particle filtering

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7 Outline Introduction General BG Modeling Description Variable Threshold Selection Experiment results Conclusion

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8 Background Modeling (1/3) Pixel-based approach: {F, M (t ), Φ, Γ} F Extracted feature for a pixel. E.g. gray/color value M (t ) Maintained BG model. M (t ) = {M S (t ), M P (t ) } S : stable P : potential

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9 Background Modeling (2/3) M (t ) = {M S (t ), M P (t ) } S: stable P: potential M1M1 M2M2 M3M3 M4M4 M5M5 M S (t ) M P (t ) C. Stauffer and W.E.L. Grimson, “ Adaptive Background Mixture Models for Real-time Tracking, ” Proc. CVPR, 1999.

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10 Background Modeling (3/3) Φ A function to judge whether a pixel is BG. {1,0} ← Φ[ F (q ), M S (t ), T ] Output: BG (1), FG (0) Γ A function to update M M (t+1) ← Γ[ F (q ), M (t ), T ] M (t+1) = {M S (t+1), M P (t+1) }

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11 Goal To avoid two situations False positive (strict T ) False negative (loose T ) Particle filtering is used To choose a suitable T, according to tracking result.

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12 Outline Introduction General BG Modeling Description Variable Threshold Selection Experiment results Conclusion

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13 Color histogram of object To calculate color histogram O t of object region {u i j } i = 1,…,n; j ∈ { R, G, B } : intensity value i : location of a pixel u of incoming image I t j : color channel Each channel has 16 bins C : normalization term To ensure: Kronecker delta function b : u i j → { 1, …, K }, K = 16 * 3 = 48 Mapping function

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14 Particle Filtering (1/3) Particle Filtering: Kalman Filter an efficient recursive filter that estimates the state of a dynamic system from a series of incomplete and noisy measurements. An example application: Providing continuously-updated information about the position and velocity of an object given only a sequence of observations about its position, each of which includes some error. It is used in a wide range of engineering applications from radar to computer vision. based on linear dynamical systems discretised in the time domain. being modelled on a Markov chain built on linear operators perturbed by Gaussian noise.

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15 Particle Filtering (2/3) Particle Filtering: Kalman Filter: Bayesian Filter Estimating the Posterior. F : state transition model (applied to previous state x k−1 ) w : process noise H : observation model ( maps true state to observed space ) v : observation noise

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16 Particle Filtering (3/3) Resampling Time = t Time = t +1 p(x t+1 |x t ) p(z t+1 |x t+1 ) p(x t+1 |x t,z t+1 ) p(x t+1 |x t ) p(z t+1 |x t+1 )

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17 Dynamic model Posterior p(x t+1 |x t, z t+1 ) is inferred by a set of N particles S t = {s t (n), π t (n) } S t : value of state x t π t : corresponding sampling probability Brownian motion is used as dynamic model s t+1 (n) = s ’ t (n) + v t v t ～ Ν(0, Σ)

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18 Observation model (1/4) for Variable threshold selection. Four color histograms are constructed: O t : tracked object at time t Ref t BG : BG region of reference BG image I t+1 FG : FG region of incoming image I t+1 I t+1 BG : BG region of incoming image I t+1 FG BG I t+1

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19 Observation model (2/4) Ref t BG I t+1 BG I t+1 FG OtOt For a particle: T = s t+1 (n) Compare similarity

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20 Observation model (3/4) To measure the similarity between two histograms Bhattacharyya distance is used h 1 (i), h 2 (i): i th bin value of h 1 and h 2

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21 Observation model (4/4) Observation model is defined as: α: user-defined parameter (0 ≦ α ≦ 1) Threshold T will be selected Choose s t+1 (n ) with max π t+1 (n ) over all N particles I t+1 FG is then calculated and used for updating O t

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22 Framework Particle Filtering T1T1 T2T2 T 10 I t+1 I t+1 FG I t+1 BG OtOt Ref t BG Similarity measurement processing Threshold selection update O t by I t+1 FG processing output result with bestfit T

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23 Outline Introduction General BG Modeling Description Variable Threshold Selection Experiment results Conclusion

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24 Benchmark sequences Number of particles used: 10

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25 Experiment results (1/3)

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26 Experiment results (2/3)

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27 Experiment results (3/3)

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28 Outline Introduction General BG Modeling Description Variable Threshold Selection Experiment results Conclusion

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29 Conclusion A method for integrating BG modeling and Object tracking is presented. Color histogram: Used as appearance model for tracking. Particle Filtering: Used to get discriminative T according to tracking result. Experiment results: Show that performance can be improved.

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30 Thank you Thanks for your listening.

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