Tracking of objects with known color signature - ELITECH 20

Tracking of objects with known color signature

Lukáš Tencerlukas.tencer@gmail.com

Content

• Overview of existing approaches• Motivation• Basic apparatus• Our approach:

– Preprocessing– Image segmentation– Object identification– Object tracking

• Testing and Results• Future work

Previous work

• ACM Survey [Yilmaz et al., 2004], after this emphasis on accuracy, speed, robustness

• Kernel based tracking

tracking kernel of image region

• Point Based Tracking

Previous work 2

• Structure Based Tracking• uses skeleton/silhouette

• Our previous work• Gesture-based

mouse control

Basic apparatus

• Color signature:– artificial representation of objects projection into

digital space, based on its color values– for our method, it is ranged values in HSV color model

• Convex hull of set of points:– Polygon, for which every point from input set lies “inside” the

polygon– minimal convex set containing input set

Our approach

• Preprocessing– Background subtraction

• Image Segmentation– Image histogramization

• Object Identification– Color contribution condition, border conditions

• Object Tracking– Bumper-region based tracking

Preprocessing

• Background subtraction• Color difference between trained background and

input image• Could cause loss of image information

Image segmantation

• Image Histogramization– Segment image into sub-regions (fixed/adaptive)– Extract descriptive channel of the image– In our case, HUE channel of HSV image model– Create histogram with n dimensions for each sub-region

Object Identification

• Select regions corresponding to object’s projection into image plane

• For one colored object, we select minimal level of dominant dimension

• For two colored object, minimal level of contribution of other color

• Border regions, maximal contribution of other colors and minimal connectivity condition

Object tracking

• Pick center of the convex hull of identified regions

• Place tracking object inside the convex hull

• Once object is moved, we identify regions, which are no longer covered by object

• Create convex hull of uncovered regions, connect it’s center and center of tracking object to identify movement vector

• Move tracking object in direction of movement vector, until all regions are covered by object regions

Testing and Results

• Improved tracking speed• Robustness against disruptive elements in scene• Unique color signature• Approach of image histogramization• Bumper-region based object tracking• Method could be used for tracking of colored object,

HCI, surveillance …

Future Work

• Use of more adaptive object representation structures, to improve computational speed– Quad-tree?

• Remove requirement to have prior information about color signature– Use training algorithm, to create model from identified

foreground

Thank you for you attention

Questions?