Multiview Image Segmentation and Video Tracking - Video Segmentation and Its Applications - page 132

Fig. 5.11 Overview of multiview video tracking approaches

Tabl e 5. 1 Feature-based tracking approaches
Ref. Features Tracker Rep.
IT NF [ 3 ] Color, depth, motion No FM
FCV [ 27 ] Color, height, width, stereo Kalman filter BB
[ 49 ] Shape, appearance, depth, motion Kalman filter BB
[ 40 ] Appearance, depth Bayer tracker BB
FCT [ 29 ] Luminance, color, human template, 2D position Kalman filter BB
[ 43 ] Stereo, 2D position, intensity Kalman filter FM
CT [ 21 ] Color histogram, 4D entities of rectangle Particle filter BB
[ 35 ] 5D state space of ellipse Particle filter EM
IT : Independent tracking; CT : Cooperative tracking; NF : No fusion; FCV : Fusion to common
view; FCT : Fusion to common tracker; Ref. : Reference; Rep. : Representation; FM : Foreground
mask; BB : Bounding box; EM : Ellipse model

5.3.1 Feature-Based Tracking

Feature-based tracking methods employs feature match framework with two steps:
feature extraction and feature matching. Feature-based tracking can be performed
either independently in each view or cooperatively across views. A classified sum-
mary of feature-based tracking approaches is presented in Table. 5.1 .

5.3.1.1 Independent Tracking

Independent tracking firstly implements single camera detection, segmentation, and
tracking on its own view. Based on whether the multi-camera fusion module is in-
volved or not, the independent tracking is further divided into independent tracking
without fusion and independent tracking with fusion .