Paper Info
Title
TREAT: Terse Rapid Edge-Anchored Tracklets
Abstract
Fast computation, efficient memory storage, and performance on par with standard state-of-the-art descriptors make binary descriptors a convenient tool for many computer vision applications. However their development is mostly tailored for static images. To respond to this limitation, we introduce TREAT (Terse Rapid Edge-Anchored Tracklets), a new binary detector and descriptor, based on tracklets. It harnesses moving edge maps to perform efficient feature detection, tracking, and description at low computational cost. Experimental results on 3 different public datasets demonstrate improved performance over other popular binary features. These experiments also provide a basis for benchmarking the performance of binary descriptors in video-based applications.
Year
DOI
Venue
2016
10.1109/AVSS.2016.7738078
2016 13th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)
Keywords
Field
DocType
treat,terse rapid edge-anchored tracklets,memory storage,binary descriptors,computer vision,TREAT,feature detection,video-based applications
Computer vision,Histogram,Pattern recognition,Computer science,Image processing,Feature extraction,Robustness (computer science),Artificial intelligence,Detector,Benchmarking,Binary number,Computation
Conference
ISBN
Citations 
PageRank 
978-1-5090-3812-1
0
0.34
References 
Authors
23
2
Name
Order
Citations
PageRank
Rémi Trichet1277.32
Noel E. O'Connor22137223.20