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Robust Multi-Pedestrian Tracking in Thermal-Visible Surveillance Videos


blob array. Related Work. Fusion background model: ... M. Isard, J. MacCormick, 'BraMBLe: a Bayesian multiple-blob tracker', ICCV 2001 ... – PowerPoint PPT presentation

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Title: Robust Multi-Pedestrian Tracking in Thermal-Visible Surveillance Videos

Robust Multi-Pedestrian Tracking in
Thermal-Visible Surveillance Videos
  • Alex Leykin and Riad Hammoud

  • Create a pedestrian tracker that operates in
  • Varying illumination conditions
  • Crowded environment
  • To achieve it we create a fusion pedestrian
    tracker that uses input from
  • IR camera
  • RGB camera
  • Our approach consists of two stages

adaptive fusion background model
Bayesian tracker
blob array
Related Work
  • Fusion background model
  • Y.Owechko, S.Medasani, and N.Srinivasa
    Classifier swarms for human detection in
    infrared imagery, OTCBVS 2004
  • M.Yasuno, N.Yasuda, andM.Aoki Pedestrian
    detection and tracking in far infrared images
    OTCBVS 2004
  • C. Dai, Y. Zheng, X. Li Layered Representation
    for Pedestrian Detection and Tracking in Infrared
    Imagery OTCBVS 2005
  • J.Davis, V.Sharma Fusion-based Background
    Subtraction Using Contour Saliency, OTCBVS 2005
  • Bayesian formulation
  • J. Deutscher, B. North, B. Bascle and A. Blake
    Tracking through singularities and
    discontinuities by random sampling, ICCV 1999
  • A. Elgammal and L. S. Davis, Probabilistic
    Framework for Segmenting People Under Occlusion,
    ICCV 2001.
  • M. Isard, J. MacCormick, BraMBLe a Bayesian
    multiple-blob tracker, ICCV 2001
  • T. Zhao, R. Nevatia Tracking Multiple Humans in
    Crowded Environment, CVPR 2004

Background Model
  • Two stacks of codeword values (codebooks)
  • Color
  • µRGB
  • Ilow
  • Ihi
  • Thermal
  • thigh
  • tlow

Adaptive Background Update
  • Match pixel p to the codebook b

I(p) gt Ilow I(p) lt Ihigh (RGB(p) µRGB) lt TRGB
t(p)/thigh gt Tt1 t(p)/tlow gt Tt2
  • If there is no match create new codeword
  • Else update the codeword with new pixel
  • If gt1 matches then merge matching codewords
  • Remove the codeword if it had not appeared for a
    prolonged period of time
  • Discard infrequent codewords
  • Exclude p from update if it corresponds to a
    currently tracked body

Subtraction Results
Color model only
Combined color and thermal model
  • Location of each pedestrian is estimated
    probabilistically based on
  • Current image
  • Model of pedestrians
  • Model of obstacles

The goal of our tracking system is to find the
candidate state x (a set of bodies along with
their parameters) which, given the last known
state x, will best fit the current observation z
P(x z, x) P(zx) P(xx)
Tracking Accepting the State
x and x ? candidate and current states
P(x) ? stationary distribution of Markov chain
mt ? proposal distribution
Candidate proposal state x is drawn with
probability mt(xx) and then accept it with the
probability a(x, x)
Tracking Priors
Constraints on the body parameters
N(hµ, hs2) and N(wµ,ws2) ? body width and height
Temporal continuity
d(wt, wt-1) and d(ht, ht-1) ? variation from the
previous size
N(µdoor, sdoor) ? distance to the closest door
(for new bodies)
Tracking Likelihoods Distance weight plane
Problem blob trackers ignore blob position in 3D
(see Zhao and Nevatia CVPR 2004)
Solution employ distance weight plane Dxy
Pxyz, Cxyz where P and C are world coordinates
of the camera and reference point correspondingly
Tracking Likelihoods Z-buffer
0 background, 1furthermost body, 2 next
closest body, etc
Tracking Likelihoods
Color observation likelihood is based on the
Bhattacharya distance between candidate and
observed color histograms
Implementation of z-buffer (Z) and distance
weight plane (D) allows to compute multiple-body
configuration with one computationally efficient
step. Let I - set of all blob pixels O - set
of body pixels Then
Tracking Jump-Diffuse Transitions
  • Add a new body
  • Delete a body
  • Recover a recently deleted body
  • Change body dimensions
  • Change body position

Tracking Anisotropic Weighted Mean Shift
Classic Mean-Shift
Our Mean-Shift
Sequence Frames People People missed Frames missed False hits Frames in false hits Identity switches
1 1054 15 3 20 1 1 3
2 0601 8 0 0 3 2 4
3 1700 16 5 10 14 5 15
4 1506 3 0 0 0 0 1
5 2031 2 0 0 0 0 2
6 1652 4 0 0 0 0 1
100 100 16 5 6.2 1.3 8.9
  • A method to fuse visible and thermal inputs for
    background model creation
  • robust to illumination changes
  • adaptive
  • computationally efficient (30fps)
  • A novel formulation of priors in MCMC particle

Future Work
  • Extend binary background mask with foreground
    probability values
  • Incorporate these probabilities into
    appearance-based fitness equation for particle
    filter-based tracker
  • Utilize tracklet stitching to decrease the number
    of broken paths

Thank you!