TY - GEN
T1 - A framework for inter-camera association of multi-target trajectories by invariant target models
AU - Daliyot, Shahar
AU - Netanyahu, Nathan S.
PY - 2013
Y1 - 2013
N2 - We propose a novel framework for associating multi-target trajectories across multiple non-overlapping views (cameras) by constructing an invariant model per each observed target. Ideally, these models represent the targets in a unique manner. The models are constructed by generating synthetic images that simulate how targets would be seen from different viewpoints. Our framework does not require any training or other supervised phases. Also, we do not make use of spatiotemporal coordinates of trajectories, i.e., our framework seamlessly works with both overlapping and non-overlapping field-of-views (FOVs) as well as widely separated ones. Also, contrary to many other related works, we do not try to estimate the relationship between cameras that tends to be error prone in environments like airports or supermarkets where targets wander about different areas, stop at times, or turn back to their starting location. We show the results obtained by our framework on a rather challenging dataset. Also, we propose a black-box approach based on Support Vector Machine (SVM) for fusing multiple pertinent algorithms and demonstrate the added value of our framework with respect to some basic techniques.
AB - We propose a novel framework for associating multi-target trajectories across multiple non-overlapping views (cameras) by constructing an invariant model per each observed target. Ideally, these models represent the targets in a unique manner. The models are constructed by generating synthetic images that simulate how targets would be seen from different viewpoints. Our framework does not require any training or other supervised phases. Also, we do not make use of spatiotemporal coordinates of trajectories, i.e., our framework seamlessly works with both overlapping and non-overlapping field-of-views (FOVs) as well as widely separated ones. Also, contrary to many other related works, we do not try to estimate the relationship between cameras that tends to be error prone in environments like airports or supermarkets where targets wander about different areas, stop at times, or turn back to their starting location. We show the results obtained by our framework on a rather challenging dataset. Also, we propose a black-box approach based on Support Vector Machine (SVM) for fusing multiple pertinent algorithms and demonstrate the added value of our framework with respect to some basic techniques.
UR - https://www.scopus.com/pages/publications/84875984169
U2 - 10.1007/978-3-642-37484-5_31
DO - 10.1007/978-3-642-37484-5_31
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AN - SCOPUS:84875984169
SN - 9783642374838
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 372
EP - 386
BT - Computer Vision - ACCV 2012 International Workshops, Revised Selected Papers
PB - Springer Verlag
T2 - 2012 International Workshop on Computer Vision with Local Binary Pattern Variants, LBP 2012, 2012 Workshop on Computational Photography and Low-Level Vision, 2012 Workshop on Developer-Centered Computer Vision, 2012 Workshop on Background Models Challenge, BMC 2012, 2012 Workshop on e-Heritage, 2012 Workshop on Color Depth Fusion in Computer Vision, 2012 Workshop on Face Analysis: The Intersection of Computer Vision and Human Perception, 2012 Workshop on Detection and Tracking in Challenging Environments, DTCE 2012, 2012 International Workshop on Intelligent Mobile Vision, IMV 2012, held in conjunction with the 11th Asian Conference on Computer Vision, ACCV 2012
Y2 - 5 November 2012 through 6 November 2012
ER -