Detection and Tracking of Moving Objects from a Moving Platform
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1 Detection and Tracking of Moving Objects from a Moving Platform Gérard Medioni Institute of Robotics and Intelligent Systems Computer Science Department Viterbi School of Engineering University of Southern California
2 Problem Definition Scenario: rigidly moving objects + moving camera Goal Motion segmentation: motion regions / background area Tracking of multiple objects: consistent track(s) over time Geo-registration and Geo-tracking: Geo-referenced mosaic and tracks
3 Scenario example 1 moving cameras Moving cameras Image stabilization Motion segmentation Tracking Mosaic +Tracks Mosaic +Tracks
4 Scenario example 2 - moving cameras with a map Map Moving camera Image stabilization Geo registration Global data association Motion segmentation Tracking Geo-referenced mosaic + tracks
5 Challenges & Applications Information sources Pixel colors + 2D coordinates Object model information (optional) Difficulties Camera motion 3D Static structures (parallax) Multiple moving objects Applications Video surveillance Video compression and indexing
6 Outline Introduction 2D Motion segmentation Tracking of multiple moving objects Geo-registration and geo-tracking Summary and Discussion
7 Motion Segmentation Overview Task: to segment motion region and background Assumptions General camera motion Distant scene Textured background
8 Feature Extraction & Matching Salient parts of the scene Extraction Harris corners Multi-scale Multi-orientation Sub-pixel accuracy Matching Small inter-frame motion Gray-scale windows Cross correlation Large viewpoint change Gradient histogram Vector angle
9 Multiple Image Registration Frame motion model Assumptions: Small inter-frame motion Distant planar scene 2D affine transform Ap 1 = p 2 A A A A A A u1 v1 1 = u2 v2 1 Robust estimation Random Sample Consensus (RANSAC) Keep the model with the largest number of inliers Non-linear refinement over the inliers
10 Motion Segmentation Two-frame pixel-level segmentation? Segmentation within a temporal window Accumulate the pixels warped from adjacent frames K-Means to find the most representative pixel Frame differencing and thresholding: I original -I model >ΔI Frame t Frame t-w t: reference frame w: half size of the window Frame t+w 10/72
11 Experimental Results (1) Original Images Initial Detection Results Motion Prob. Maps Tracking Results 11/72
12 Experimental Results (2) Original images Initial Detection Results Motion Prob. Maps Tracking Results
13 Experimental Results (3) A synthesized video without motion regions
14 Outline Introduction 2D Motion segmentation Tracking of multiple moving objects Geo-registration and geo-tracking Summary and Discussion
15 Problem statement- multiple target tracking Input: foreground regions in each frame Output: trajectories with consistent track IDs Challenges: Noisy foreground regions Occlusions
16 Problematic underlying assumption One-to-one assumption One target can correspond to at most one observation One observation can be associated to at most one target Appropriate to punctual observations Underlying one-to-one assumption may not stand for visual tracking Radar UAV camera Stationary camera
17 Related work (Pasula et al., 99) Gibbs sampling to compute joint DA MAP, multi-scan, uniform prior (no missing or false detection) (Cong et al., 04) Approximate association probabilities in JPDAF MMSE, MCMC outperforms JPDAF, one-scan/muliti-scan (Sastry, et.al 04) MCMC to compute joint DA with unknown number of targets MAP, multi-scan, outperforms MHT, consider temporal association only (F.Dellaert et.al 03) MCMC to SfM without correspondence MMSE, Single scan, similar to JPDAF Our method: overcome the one-to-one assumption MAP, multi-scan, consider both spatial and temporal association One-to-one assumption
18 Anatomy of the problem Explain foreground regions: It is hard at one frame without using any model information It is solvable if smoothness in motion and appearance is used
19 Explanation of foreground regions Two way of explain foreground regions Precisely Approximately Labeling of foreground regions The label(s) of a pixel indicates the track ID Each pixel can have multiple labels to represent occlusions Accurate but expensive! Cover of foreground regions A set of shapes (rectangles) Each rectangle can have overlap with others to represent occlusions Approximate but Efficient!
20 Our formulation Given A set of noisy observations (foreground regions) Find A cover ω of foreground regions over time τ k is a sequence of shapes (rectangles)
21 Solution space Solution space Ω is a collection of spatio-temporal covers of observation Y. Joint association event Two kinds of data association { } ω = τ1, τ 2K, τ K Spatial data association - change the cover at one instant Temporal data association - form consistent tracks Uncovered area belongs to false alarms (a) Observations Y (b) One possible cover of Y
22 Bayesian formulation MAP estimate Prior model p(ω) ω* = arg max( p( ω Y )) p( ω Y ) p( Y ω) p( ω) Few number of long tracks One track should have little overlapping with other track unless necessary p( ω ) = p( L) p( K) p( O) Likelihood p(y ω) Smoothness in both motion and appearance Areas of uncovered false alarms p(f) K τ 1 k = p( Y ω) p( F) L( τ ( t ) τ ( t )) k = 1 i= 1 k i+ 1 k i Motion likelihood Appearance likelihood
23 Motion and appearance likelihood Motion k k k x = t 1 A x + + t w y = H x + v k k k t t w ~ N(0, Q) v ~ N(0, R) ( t ) τ k i+ 1 τ k ti+ 1 ( ) Appearance L ( τ ( t ) τ ( t )) p( τ ( t ) τ ( t )) M k i+ 1 k i k i+ 1 k + 1 i ( ) ( ) L ( τ ( t ) τ ( t )) = 1/ z exp λ D( τ ( t ), τ ( t ) A k i+ 1 k i 3 3 k i k i+ 1 D( τ k ( ti ), τ k ( ti+ 1) Kullback- Leibler (KL) distance between two RGB color histograms
24 MAP of full posterior p(ω Y) MAP estimate of such a posterior is not a trivial task Even to determine the parameters in such a posterior is not an easy task { C C C C C } p Y S K F S S S ( ω ) exp 0 len olp 4 app mot MAP is equivalent to minimize an energy function. Solution to MAP: Sampling based method to avoid enumerating all possible solutions Two types of proposal moves (temporal and spatial moves) Symmetric temporal information
25 Markov Chain Monte Carlo Basic idea: construct a Markov chain which will converge to the target distribution State of the Markov chain is defined in Ω Transition of the Markov chain is guided by a proposal distribution Metropolis-Hasting algorithm Propose a new state ω from the previous state ω (i) Accept ω with probability Properties min 1, ω ω ω ( i) ' ~ q( ' ) ( i) ( ω ') q( ω ω ') ( i) ( i) ( ) q( ' ) p p ω ω ω Don t have to compute the global p(ω), but the local ratio p(ω )/ p(ω) For MAP, don t have to keep the whole chain, but the current state and the best one
26 Metropolis-Hasting algorithm (0) 1. Initialize ω. 2. For i = 0 to N -1 - Sample u U[0,1] ω ω ω - Compute A( ω, ')= n 1, ( i) - Propose ' q( ' ). ω ω ω p( ω ) q( ω ' ω ) ( i) ( i) p( ') q( ') ω mi ( i) ( i) u < A ω ω ω = ω ( i) ( i+ 1) - If (, ') ' else Endfor ω = ω ( i+ 1) ( i) ω ω ω N is the length of Markov chain (0) ( ) The chain {, K N, } N p( ) q() is called the proposal distribution
27 Two types of q(ω ω) Temporal moves and spatial moves to drive the Markov chain Data-driven proposal q( ω ' ω) q( ω ' ω, D) Spatial moves are made only after enough temporal information is collected Temporal Moves Birth/Death Extension/ Reduction Split/Merge Switch Symmetric temporal information Forward and backward (e.g. extension) Deal with occlusions at the very beginning Spatial Moves Segmentation /Aggregation Diffusion
28 MCMC Data Association (0) 1. Initialize ω. 2. For i = 0 to N -1 - Sample u U[0,1] ( i) - Sample if i < ε N, ω ' qtemporal ( ω ' ω ) ω ω ω ( i) else ' qall ( ' ). - Compute A( ω, ')= n 1, ω ω ω p( ω ) q( ω ' ω ) ( i) ( i) p( ') q( ') ω mi ( i) ( i) u < A ω ω ω = ω ( i) ( i+ 1) - If (, ') ' else Endfor ω = ω ( i+ 1) ( i)
29 Determining Parameters Determine the parameters in the full posterior Casual setting makes ground truth p(ω gt Y) even much lower than the solution. Take advantage of the property of MCMC { C C C C C } p Y S K F S S S ( ω ) exp 0 len olp 4 app mot p( ωgt ) Degenerate the ω gt to ω p( ω ') A[ C0, C1, C2, C3, C4 ] b C0, C1, C2, C3, C4 0 max( C0 + C1 + C2 + C3 + C4) 1 Linear Programming to solve it (GNU Linear Programming Kit)
30 Simulation experiments Settings K (unknown number) moving discs in 200x200 Independent color appearance and motion Static occlusion and inter-occlusion False alarms Original video Tracking result
31 Simulation experiments Quantitative comparison MHT (I. Cox94), JPDAF (J.Kang03), Temporal only STDA score in VACE-II eval Same motion and appearance likelihood Average of multiple sequence and multiple runs FA=0, W=50, 10K MCMC iterations K=5, W=50, 10K MCMC iterations
32 Simulation experiments Online implementation Sliding window W Initialize ω t with ω* t-1 Online vs. offline comparison T=1000
33 Real Scenarios
34 Experiments CLEAR 320x240 Vivid-II 320x240
35 Experiments Can handle occlusion at the beginning by using symmetric temporal information
36 Outline Introduction 2D Motion segmentation Tracking of multiple moving objects Geo-registration and geo-tracking Summary and Discussion
37 Geo-registration Use 2D homography to compensate inter-frame (2- view) motion H = ( H ) H H 1 i+ 1, M i, i+ 1 i, M update H i,i+1 H i,m H i+1,m H update Refine the homography between map and images 37/72
38 Geo-registration results Geo-mosaicing 2000 frames on top of the reference frame.
39 Experimental results Results are shown on two UAV data sets Map is acquired from Google Earth Geo-registration is performed every 50 frames Local data association (MCMCDA) window 50 frames
40 Geo-registration Without geo-refinement With geo-refinement
41 Experimental results
42 Experimental results
43 System implementation C++ implementation Xeon Dual Core P4 3.0GHz Preliminary time performance Procedure Time (seconds) on 320x240 Image registration ~ 0.25 Motion detection (moving cameras) Object detection after motion segmentation Geo-registration ~ (2 / 0.1) (CPU / GPU) ~0.25 ~ 6 every 50 frames Tracking ~ 0.4 Total ~ 1 ( GPU) 43/72
44 Outline Introduction 2D Motion segmentation Tracking of multiple moving objects Geo-registration and geo-tracking Summary and Discussion
45 Summary & Discussion Detection and tracking in dynamic scene Moving camera + rigid moving objects 2D motion segmentation and geometric analysis of background Spatial and temporal (2D+t) data association of moving objects Tracking with Geo-registration Highlights Solution to practical problems in detection and tracking area Encouraging results and extensive applications Future directions Multi-view geometry + object recognition Automatically determination of applicable tasks
46 Reference Qian Yu and Gérard Medioni, A GPU-based implementation of Motion Detection from a Moving Platform, to appear in IEEE workshop on Computer Vision on GPU, in conjunction with CVPR 08 Qian Yu and Gérard Medioni, Integrated Detection and Tracking for Multiple Moving Objects using Data-Driven MCMC Data Association, IEEE Workshop on Motion and Video Computing (WMVC'08), 2008 Qian Yu, Gérard Medioni, Isaac Cohen, "Multiple Target Tracking Using Spatio-Temporal Monte Carlo Markov Chain Data Association" IEEE Conference on Computer Vision and Pattern Recognition, 2007 (CVPR'07), pp.1-8 Qian Yu, Gérard Medioni, "Map-Enhanced Detection and Tracking from a Moving Platform with Local and Global Data Association," IEEE Workshop on Motion and Video Computing (WMVC'07), 2007 Yuping Lin, Qian Yu, Gerard Medioni "Map-Enhanced UAV Image Sequence Registration" Workshop on Applications of Computer Vision (WACV'07), 2007 Qian Yu, Isaac Cohen, Gérard Medioni and Bo Wu "Boosted Markov Chain Monte Carlo Data Association for Multiple Target Detection and Tracking," Proceedings of the 18th international Conference on Pattern Recognition (ICPR'06), Vol. 2, pp
47 Q&A Thank you!
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