Title
Tracking vulnerable road users with severe occlusion by adaptive part filter modeling
Abstract
The visual based tracking has been an active area among the research community in recent years. Its application can be broadly found in intelligent transportation systems like advanced driver assistance systems (ADAS) or autonomous vehicles. One objective is to monitor the trajectory and behavior of the vulnerable road users (VRUs), e.g., pedestrians and cyclists, to prevent the probable collisions. However, the tracking of VRUs is still challenging, especially in cases, where severe occlusion occurs and the tracker fails due to the abrupt change of object appearance. To tackle this problem we propose a new tracking approach leveraging the part based trackers, which are built based on correlation filters. In this method both the number and the size of part filters are adapted to the current appearance of the object, to eliminate the influence by occluded parts. Experimental results show that our tracker performs robust with respect to occlusions, especially in cases, where long term and severe occlusions appear. Due to a sophisticated design, a real time processing performance can also be achieved by our approach.
Year
DOI
Venue
2017
10.1109/ICVES.2017.7991915
2017 IEEE International Conference on Vehicular Electronics and Safety (ICVES)
Keywords
Field
DocType
vulnerable road user tracking,occlusion,adaptive part filter modeling,visual based tracking,intelligent transportation systems,advanced driver assistance systems,ADAS,autonomous vehicles,trajectory monitoring,VRU behavior,collision prevention,object appearance,part based trackers,correlation filters,real time processing performance
BitTorrent tracker,Computer vision,Occlusion,Support vector machine,Advanced driver assistance systems,Feature extraction,Artificial intelligence,Intelligent transportation system,Engineering,Trajectory
Conference
ISBN
Citations 
PageRank 
978-1-5090-5678-1
0
0.34
References 
Authors
27
2
Name
Order
Citations
PageRank
Wei Tian12810.31
Martin Lauer2218.98