CAMREP- Concordia Action and Motion Repository

TL;DR

CAMREP is a comprehensive, multi-modal repository of motion and action data designed to facilitate research in action recognition, gait analysis, and related fields by providing diverse and large-scale datasets.

Contribution

The paper introduces CAMREP, a new large-scale, multi-modal motion and action database that addresses data accessibility barriers in research.

Findings

Contains diverse modalities like video and motion capture data

Includes large and long-duration datasets

Enables multi-modal analysis for various applications

Abstract

Action recognition, motion classification, gait analysis and synthesis are fundamental problems in a number of fields such as computer graphics, bio-mechanics and human computer interaction that generate a large body of research. This type of data is complex because it is inherently multidimensional and has multiple modalities such as video, motion capture data, accelerometer data, etc. While some of this data, such as monocular video are easy to acquire, others are much more difficult and expensive such as motion capture data or multi-view video. This creates a large barrier of entry in the research community for data driven research. We have embarked on creating a new large repository of motion and action data (CAMREP) consisting of several motion and action databases. What makes this database unique is that we use a variety of modalities, enabling multi-modal analysis. Presently, the size of datasets varies with some having a large number of subjects while others having smaller numbers. We have also acquired long capture sequences in a number of cases, making some datasets rather large.