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?
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