GMCP-Tracker: Global Multi-object Tracking Using Generalized Minimum Clique Graphs

By Amir Zamir, Afshin Dehghan, Mubarak Shah. European Conference on Computer Vision, 2012

408 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Ocean Engineering

View Paper: https://www.semanticscholar.org/paper/d95d505a959c9fc8090cb84287dce92128fce63e

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In Computer Vision and Pattern Recognition

In Ocean Engineering

Citations in the corpus

Deep Network Flow for Multi-object Tracking (2017, neutral): “[53] include appearance information via color histograms.”
MOT16: A Benchmark for Multi-Object Tracking (2016, background): “The easiest sequence for tracking (S2L1) consisted of a scene with few pedestrians, and for that sequence state-of-the-art methods perform extremely well with accuracies of over 90% given a good set of initial detections [23], [33], [47].”
Tracking Without Bells and Whistles (2019, methodology): “Alternative formulations typically lead to more involved optimization problems, including minimum cliques [65], general-purpose solvers like MCMC [64] or multi-cuts [59].”
Space-Time Correspondence as a Contrastive Random Walk (2020, background): “This can be addressed in a variety of ways, including dynamic programming, particle ﬁltering, various graph-based combinatorial optimization, and more recently, graph neural networks [96, 69, 6, 66, 12, 94, 38, 37, 40, 9].”
Image Geo-Localization Based on MultipleNearest Neighbor Feature Matching UsingGeneralized Graphs (2014, methodology): “It has been utilized for maximizing the number of comparisons between human detections in different video frames in order to perform data association as well [28].”
Deep Affinity Network for Multiple Object Tracking (2018, methodology): “In contrast to local methods, global techniques [39], [40],”
Joint Graph Decomposition & Node Labeling: Problem, Algorithms, Applications (2016, background): “ns deﬁne both a decomposition and a node labeling of a givengraph(Fig. 1). This problem offers a common mathematical abstraction of seemingly unrelated computer vision tasks: Multiple object tracking [2, 3, 4, 7, 13, 17, 19, 29, 30] can be seen as the task of deciding, for every point in an image, w”
Joint Object Detection and Multi-Object Tracking with Graph Neural Networks (2021, background): “On the other hand, batch methods [46]–[51] need to have access to global information (i.”
FairMOT: On the Fairness of Detection and Re-identification in Multiple Object Tracking (2020, methodology): “…such as group models and RNNs. Offline Methods The offline methods (or batch methods) (Zhang et al. 2008; Wen et al. 2014; Berclaz et al. 2011; Zamir et al. 2012; Milan et al. 2013; Choi 2015; Brasó and Leal-Taixé 2020; Hornakova et al. 2020) often achieve better results by performing global…”
Category-Blind Human Action Recognition: A Practical Recognition System (2015, methodology): “We also observe that although SimpleComb uses the CHARM as a dedicated approach to recognize twoperson interactions, its two-person interaction accuracy is lower than that of our CHARM.”
Learning People Detectors for Tracking in Crowded Scenes (2013, methodology): “People detection is a key building block of most stateof-the-art people tracking methods [3, 22, 23].”
Online Multi-object Tracking Using CNN-Based Single Object Tracker with Spatial-Temporal Attention Mechanism (2017, methodology): “These off-line methods consider MOT as a global optimization problem and focus on designing various optimization algorithm such as network flow [39, 58], continuous energy minimization [33], max weight independent set [6], k-partite graph [56, 10], subgraph multi-cut [43, 44] and so on.”
Subgraph decomposition for multi-target tracking (2015, methodology): “For example, it is common to first group single-frame hypotheses into tracklets spanning several adjacent frames, and then combine them into complete tracks [12, 19, 24, 11, 22, 23, 2].”
Learning Social Etiquette: Human Trajectory Understanding In Crowded Scenes (2016, background): “Over the past decade, Multi-Target Tracking (MTT) algorithms have made great progress in solving the data association problem as a graph theoretic problem [34,35,36,30,37,38].”
Multiple People Tracking by Lifted Multicut and Person Re-identification (2017, background): “Recent works on multi-person tracking focus on the tracking-by-detection approach [21, 36, 35, 29, 30].”