Paper Info
Title
Dynamic multi-level appearance models and adaptive clustered decision trees for single target tracking.
Abstract
A robust tracking algorithm for tracking arbitrary objects in challenging video sequences.An adaptive clustered decision tree approach which dynamically selects the minimum combination of features to represent target parts.This adaptive clustered decision tree is utilized both to enable robust matching at the part level and to select the new parts for learning. This paper presents a tracking algorithm for arbitrary objects in challenging video sequences. Targets are modelled at three different levels of granularity (pixel, parts and bounding box levels), which are cross-constrained to enable robust model relearning. The main contribution is an adaptive clustered decision tree method which dynamically selects the minimum combination of features necessary to sufficiently represent each target part at each frame, thereby providing robustness with computational efficiency. The adaptive clustered decision tree is used in two separate ways: firstly for parts level matching between successive frames; secondly to select the best candidate image regions for learning new parts of the target. We test the tracker using two different tracking benchmarks (VOT2013-2014 and CVPR2013 tracking challenges), based on two different test methodologies, and show it to be more robust than the state-of-the-art methods from both of those tracking challenges, while also offering competitive tracking precision. Additionally, we evaluate the contribution of each key component of the tracker to overall performance; test the sensitivity of the tracker under different initialization conditions; investigate the effect of using features in different orders within the decision trees; illustrate the flexibility of the method for handling arbitrary kinds of features, by showing how it easily extends to handle RGB-D data.
Year
DOI
Venue
2017
10.1016/j.patcog.2017.04.001
Pattern Recognition
Keywords
Field
DocType
Single target tracking,Adaptive clustered decision trees,Multi-level appearance models
Decision tree,Computer vision,Pattern recognition,Computer science,Robustness (computer science),Pixel,RGB color model,Artificial intelligence,Granularity,Initialization,Machine learning,Minimum bounding box
Journal
Volume
Issue
ISSN
69
C
0031-3203
Citations 
PageRank 
References 
2
0.35
26
Authors
3
Name
Order
Citations
PageRank
Jingjing Xiao151.76
Rustam Stolkin252739.74
Ales Leonardis31636147.33