A soft-biometrics dataset for person tracking and re-identification

In this work we present a new dataset for the tasks person detection, tracking, re-identification, and soft-biometric attribute detection in surveillance data. The dataset was recorded over three days and consists of more than 30 individuals moving through a network of seven cameras. Person tracks are labeled with consistent IDs as well as soft-biometric attributes, such as a description of the clothing, gender, or height. Persons in the video data alter their appearance by changing clothes or wearing accessories. A second, clothing specific ID of each track allows for the evaluation of re-identification with or without the presence of clothing changes. In addition to video and camera calibration data, we provide evaluation protocols, tools and baseline results for each of the four tasks.

[1]  J. Crowley,et al.  CAVIAR Context Aware Vision using Image-based Active Recognition , 2005 .

[2]  Jitendra Malik,et al.  Poselets: Body part detectors trained using 3D human pose annotations , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[3]  Larry S. Davis,et al.  Learning Discriminative Appearance-Based Models Using Partial Least Squares , 2009, 2009 XXII Brazilian Symposium on Computer Graphics and Image Processing.

[4]  Vittorio Murino,et al.  Custom Pictorial Structures for Re-identification , 2011, BMVC.

[5]  Hai Tao,et al.  Evaluating Appearance Models for Recognition, Reacquisition, and Tracking , 2007 .

[6]  A. Nilski.  Evaluating multiple camera tracking systems - the i-LIDS 5TH scenario , 2008, 2008 42nd Annual IEEE International Carnahan Conference on Security Technology.

[7]  Bill Triggs,et al.  Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[8]  Pietro Perona,et al.  Fast Feature Pyramids for Object Detection , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  M. Hahnel,et al.  Color and texture features for person recognition , 2004, 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541).

[10]  Rainer Stiefelhagen,et al.  Evaluating Multiple Object Tracking Performance: The CLEAR MOT Metrics , 2008, EURASIP J. Image Video Process..

[11]  Matti Pietikäinen,et al.  CMV100: A dataset for people tracking and re-identification in sparse camera networks , 2012, Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012).

[12]  Luc Van Gool,et al.  Depth and Appearance for Mobile Scene Analysis , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[13]  Sridha Sridharan,et al.  A Database for Person Re-Identification in Multi-Camera Surveillance Networks , 2012, 2012 International Conference on Digital Image Computing Techniques and Applications (DICTA).

[14]  Shaogang Gong,et al.  Attributes-Based Re-identification , 2014, Person Re-Identification.

[15]  Shaogang Gong,et al.  Person re-identification by probabilistic relative distance comparison , 2011, CVPR 2011.

[16]  Matti Pietikäinen,et al.  Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[17]  Slawomir Bak,et al.  Boosted human re-identification using Riemannian manifolds , 2012, Image Vis. Comput..

[18]  Rita Cucchiara,et al.  3DPeS: 3D people dataset for surveillance and forensics , 2011, J-HGBU '11.

[19]  Horst Bischof,et al.  Person Re-identification by Descriptive and Discriminative Classification , 2011, SCIA.

[20]  Rainer Stiefelhagen,et al.  Person tracking-by-detection with efficient selection of part-detectors , 2013, 2013 10th IEEE International Conference on Advanced Video and Signal Based Surveillance.