The CMU Motion of Body (MoBo) Database

By Ralph Gross, Jianbo Shi. 2001

658 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/599cc88971d2f5b375ff23b6342f17855e01791c

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Xiaogang Wang
2. Dacheng Tao
3. Luc Van Gool
4. Jiwen Lu
5. Yi Yang
6. Jun Liu
7. G. Wang
8. Wanli Ouyang

Citations in the corpus

• Deep Reconstruction Models for Image Set Classification (2015, background): “Index Terms—Image set classification, deep learning, auto-encoders, video based face recognition, object recognition Ç”
• Covariance discriminative learning: A natural and efficient approach to image set classification (2012, methodology): “For the face recognition task, we consider three widely studied datasets with different characteristics, including two benchmark datasets: Honda/UCSD [14], CMU MoBo [7], and one much challenging dataset: YouTube Celebrities [11], to ensure extensive evaluations.”
• Manifold Discriminant Analysis (2009, methodology): “For face recognition task, three datasets are considered: Honda/UCSD [16], CMU MoBo [10], and YouTube Celebrities [14], to ensure extensive evaluations of different methods.”
• Gait analysis for recognition and classification (2002, background): “appearance features from the CMU gait database [8].”
• Silhouette-based human identification from body shape and gait (2002, unknown): “(a and b) The CMU MoBo database [5] contains six simultaneous motion sequences of 25 subjects (23 male, 2 female) walking on a treadmill.”
• Manifold-Manifold Distance with application to face recognition based on image set (2008, methodology): “Datasets: We consider two different public datasets: Honda/UCSD [12] and CMU MoBo [6] in order to ensure an extensive evaluation of different methods against dataset changes including image resolution, facial expressions, illumination variations and the size of the dataset.”
• HumanEva: Synchronized Video and Motion Capture Dataset and Baseline Algorithm for Evaluation of Articulated Human Motion (2010, methodology): “2 where data from 7 synchronized video cameras is illustrated with an overlay of ground truth body pose.”
• Graph embedding discriminant analysis on Grassmannian manifolds for improved image set matching (2011, methodology): “CMU-MoBo consists of motion sequences of 25 people walking on a treadmill.”
• Berkeley MHAD: A comprehensive Multimodal Human Action Database (2013, methodology): “…of research groups invested time and effort in creating multiview video datasets, such as the CMU Motion of Body (MoBo) Database by Gross and Shi [10], IXMAS by Weinland et al. [32], ViHASi: Virtual Human Action Silhouette Data by Ragheb et al. [24] and i3DPost Multi-view and 3D Human…”
• Human Action Segmentation and Recognition Using Discriminative Semi-Markov Models (2011, methodology): “This dataset [26] contains 24 individuals(3) walking on a treadmill.”
• A Framework for Evaluating the Effect of View Angle, Clothing and Carrying Condition on Gait Recognition (2006, methodology): “In addition to the database in the HumanID Gait Challenge Problem [1, 8], there also exist many other gait databases, such as UCSD Database, CASIA Gait Database (Dataset A) [2], Georgia Tech Database [3], MIT AI Gait Data, CMU Mobo Database [4, 6], HID-UMD Database, and Soton Database [5, 10].”
• Improved gait recognition by gait dynamics normalization (2006, methodology): “We consider recognition rates reported on publicly available experimental protocols and data sets (> 25 persons) such as the CMU-Mobo data set [5] (indoor, 25 subjects), UMD data set [6] (outdoor, 55 subjects), SOTON Large data set [7] (indoor and outdoor, 115 subjects), HumanID Gait Challenge data…”
• MuHAVi: A Multicamera Human Action Video Dataset for the Evaluation of Action Recognition Methods (2010, methodology): “The dataset complements similar efforts such as the CMU Motion Database [1], HumanEva [2], both mostly aimed at pose recovery with motion capture ground truths.”
• Gait recognition: a challenging signal processing technology for biometric identification (2005, methodology): “The silhouettes are from CMU’s MoBo database [15].”
• The humanID gait challenge problem: data sets, performance, and analysis (2005, methodology): “We show that our baseline algorithm is a reasonable choice by reporting its performance on the CMU Mobo data set [35].”