On pairwise costs for network flow multi-object tracking
By Visesh Chari, Simon Lacoste-Julien, Ivan Laptev, Josef Sivic. 2014
140 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Automotive Engineering, Computer Vision and Pattern Recognition
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Ivan Laptev
- Josef Sivic
- Jean-Baptiste Alayrac
- Mubarak Shah
- Andrew Zisserman
- Simon Lacoste-Julien
- Cordelia Schmid
- Piotr Bojanowski
In Automotive Engineering
In Computer Vision and Pattern Recognition
- Luc Van Gool
- Yi Yang
- Andrew Zisserman
- Cordelia Schmid
- Dacheng Tao
- Xiaogang Wang
- Ming-Hsuan Yang
- Jitendra Malik
Citations in the corpus
- Deep Affinity Network for Multiple Object Tracking (2018, background): “Chari et al. [52] added a pairwise cost to the min-cost network flow framework and proposed a convex relaxation solution with a rounding heuristic for tracking.”
- Features for Multi-target Multi-camera Tracking and Re-identification (2018, background): “Methods that use all pairwise terms, not only timeconsecutive ones, are significantly more accurate but NPhard [18, 25, 27, 28, 41, 58, 61, 65, 66, 67].”
- Learning from Narrated Instruction Videos (2015, methodology): “strategy that was originally proposed by [8] and then re-used in [17]: we pick the last visited corner (which is necessarily”
- Convergence Rate of Frank-Wolfe for Non-Convex Objectives (2016, methodology): “Examples of recent applications where the FW algorithm is run on a non-convex objective include multiple sequence alignment (Alayrac et al., 2016, Appendix B) and multi-object tracking (Chari et al., 2015, Section 5.1).”
- Multiple object tracking: A literature review (2014, methodology): “This model is exceptionally popular and has been widely adopted [40, 19, 101, 45, 43, 148, 149, 150, 151].”
- Revisiting Projection-Free Optimization for Strongly Convex Constraint Sets (2018, background): “…structural SVM, computer vision, sparse PCA, metric learning, and many other settings (Jaggi, Sulovsk, and others 2010; Lacoste-Julien et al. 2013; Osokin et al. 2016; Wang et al. 2016; Chari et al. 2015; Harchaoui et al. 2012; Hazan and Kale 2012; Shalev-Shwartz, Gonen, and Shamir 2011).”