ABSTRACT
The interest of the research community in creating reference datasets for performance analysis is always very high. Although new datasets, collecting large amounts of video footage are spreading in surveillance and forensics, few bench-marks with annotation data are available for testing specific tasks and especially for 3D/multi-view analysis. In this paper we present 3DPeS, a new dataset for 3D/multi- view surveillance and forensic applications. This has been designed for discussing and evaluating research results in people re-identification and other related activities (people detection, people segmentation and people tracking). The new assessed version of the dataset contains hundreds of video sequences of 200 people taken from a multi-camera distributed surveillance system over several days, with different light conditions; each person is detected multiple times and from different points of view. In surveillance scenarios, the dataset can be exploited to evaluate people reacquisition, 3D body models and people activity reconstruction algorithms. In forensics it can be adopted too, by relaxing some constraints (e.g. real time) and neglecting some information (e.g. calibration). Some results on this new dataset are presented using state of the art methods for people re-identification as a benchmark for future comparisons.
- A. Alahi, P. Vandergheynst, M. Bierlaire, and M. Kunt. Cascade of descriptors to detect and track objects across any network of cameras. Computer Vision and Image Understanding, 114(6):624--640, 2010. Google ScholarDigital Library
- D. Baltieri, R. Vezzani, and R. Cucchiara. 3d body model construction and matching for real time people re-identification. In Proc. of EG-IT 2010, pages 65--71, 2010.Google Scholar
- D. Baltieri, R. Vezzani, and R. Cucchiara. Sarc3d: a new 3d body model for people tracking and re-identification. In Proc. of ICIAP, Ravenna, Italy, Sept. 2011. Google ScholarDigital Library
- S. Calderara, A. Prati, and R. Cucchiara. HECOL: Homography and epipolar-based consistent labeling for outdoor park surveillance. Computer Vision and Image Understanding, 111(1):21--42, 2008. Google ScholarDigital Library
- R. Cucchiara, C. Grana, M. Piccardi, and A. Prati. Detecting moving objects, ghosts and shadows in video streams. IEEE Transactions on Pattern Analysis and Machine Intelligence, 25(10):1337--1342, Oct. 2003. Google ScholarDigital Library
- M. Farenzena, L. Bazzani, A. Perina, V. Murino, and M. Cristani. Person re-identification by symmetry-driven accumulation of local features. In Proc. of CVPR, pages 2360--2367. IEEE, June 2010.Google ScholarCross Ref
- M. Fischer, H. K. Ekenel, and R. Stiefelhagen. Interactive person re-identification in TV series. In Proc. of Int'l Workshop on Content Based Multimedia Indexing (CBMI), pages 1--6, June 2010.Google ScholarCross Ref
- T. Gandhi and M. Trivedi. Panoramic Appearance Map (PAM) for Multi-camera Based Person Re-identification. In Proc. of AVSS, pages 78--78, Nov. 2006. Google ScholarDigital Library
- D. Gray, S. Brennan, and H. Tao. Evaluating Appearance Models for Recognition, Reacquisition, and Tracking. In 10th IEEE International Workshop on Performance Evaluation of Tracking and Surveillance (PETS), 2007.Google Scholar
- D. Gray and H. Tao. Viewpoint Invariant Pedestrian Recognition with an Ensemble of Localized Features. In Lecture Notes In Computer Science; Vol. 5302 - Proc. of ECCV: Part I, page 262, 2008. Google ScholarDigital Library
- L. Havasi, Z. Szlavik, and T. Sziranyi. Eigenwalks: walk detection and biometrics from symmetry patterns. In Proc. of ICIP, pages III--289, 2005.Google ScholarCross Ref
- iLids. The Image library for intelligent detection systems, 2010. scienceandresearch.homeoffice.gov.uk/hosdb/cctv-imaging-technology/i-lids/index.html.Google Scholar
- O. Javed, K. Shafique, Z. Rasheed, and M. Shah. Modeling inter-camera space-time and appearance relationships for tracking across non-overlapping views. Computer Vision and Image Understanding, 109(2):146--162, 2008. Google ScholarDigital Library
- V. Leung, J. Orwell, and S. A. Velastin. Performance evaluation of re-acquisition methods for public transport surveillance. In Proc. of ICCARV, pages 705--712. IEEE, Dec. 2008.Google ScholarCross Ref
- D. Maio, D. Maltoni, R. Cappelli, J. Wayman, and A. Jain. Fvc2000: Fingerprint verification competition. 24(3):402--412, March 2002. Google ScholarDigital Library
- A.-T. Nghiem, F. Bremond, M. Thonnat, and V. Valentin. Etiseo, performance evaluation for video surveillance systems. In Proc. of AVSS, 2007. Google ScholarDigital Library
- S. Oh, A. Hoogs, A. Perera, N. Cuntoor, C.-C. Chen, J. T. Lee, S. Mukherjee, J. K. Aggarwal, H. Lee, L. Davis, E. Swears, X. Wang, Q. Ji, K. Reddy, M. Shah, C. Vondrick, H. Pirsiavash, D. Ramanan, J. Yuen, A. Torralba, B. Song, A. Fong, A. Roy-Chowdhury, and M. Desai. A large-scale benchmark dataset for event recognition in surveillance video. In Proc. of CVPR, 2011. Google ScholarDigital Library
- P. Over, G. Awad, J. Fiscus, B. Antonishek, A. F. Smeaton, W. Kraaij, and G. Quenot. Trecvid 2010 -- an overview of the goals, tasks, data, evaluation mechanisms and metrics. In Proc. of TRECVID 2010. NIST, USA, 2011.Google Scholar
- PETS. Dataset - Performance Evaluation of Tracking and Surveillance, 2009. http://www.cvg.rdg.ac.uk/PETS2009/.Google Scholar
- P. Phillips, H. Moon, P. Rauss, and S. Rizvi. The feret evaluation methodology for face-recognition algorithms. In Proc. of CVPR, pages 137--143, 1997. Google ScholarDigital Library
- B. Prosser, W.-S. Zheng, S. Gong, and T. Xiang. Person re-identification by support vector ranking. In Proc. BMVC, pages 21.1--11, 2010. doi:10.5244/C.24.21.Google ScholarCross Ref
- N. Truongcong, C. Achard, L. Khoudour, and L. Douadi. Video sequences association for people re-identification across multiple non-overlapping cameras. Lecture Notes in Computer Science, Proceedings of ICIAP 2009, N5716:p179--189, 2009. Google ScholarDigital Library
- R. Vezzani and R. Cucchiara. Video surveillance online repository (visor): an integrated framework. Multimedia Tools and Applications, 50(2):359--380, Nov. 2010. Google ScholarDigital Library
- R. Vezzani, C. Grana, and R. Cucchiara. Probabilistic people tracking with appearance models and occlusion classification: The ad-hoc system. Pattern Recognition Letters, 32(6):867--877, Apr. 2011. Google ScholarDigital Library
Index Terms
- 3DPeS: 3D people dataset for surveillance and forensics
Recommendations
Dana36: A Multi-camera Image Dataset for Object Identification in Surveillance Scenarios
AVSS '12: Proceedings of the 2012 IEEE Ninth International Conference on Advanced Video and Signal-Based SurveillanceWe present a novel dataset for evaluation of object matching and recognition methods in surveillance scenarios. Dataset consists of more than 23,000 images, depicting 15 persons and nine vehicles. A ground truth data -- the identity of each person or ...
SOMPT22: A Surveillance Oriented Multi-pedestrian Tracking Dataset
Computer Vision – ECCV 2022 WorkshopsAbstractMulti-object tracking (MOT) has been dominated by the use of track by detection approaches due to the success of convolutional neural networks (CNNs) on detection in the last decade. As the datasets and bench-marking sites are published, research ...
VSSum: A Virtual Surveillance Dataset for Video Summary
ICCCV '22: Proceedings of the 5th International Conference on Control and Computer VisionVideo summary can greatly reduce the size of video while retaining most of the content, which is a very promising video analysis technology, especially for surveillance. However, few datasets can be used for video summaries because of the privacy and ...
Comments