Towards automated visual surveillance using gait for identity recognition and tracking across multiple non-intersecting cameras

Despite the fact that personal privacy has become a major concern, surveillance technology is now becoming ubiquitous in modern society. This is mainly due to the increasing number of crimes as well as the essential necessity to provide secure and safer environment. Recent research studies have confirmed now the possibility of recognizing people by the way they walk i.e. gait. The aim of this research study is to investigate the use of gait for people detection as well as identification across different cameras. We present a new approach for people tracking and identification between different non-intersecting un-calibrated stationary cameras based on gait analysis. A vision-based markerless extraction method is being deployed for the derivation of gait kinematics as well as anthropometric measurements in order to produce a gait signature. The novelty of our approach is motivated by the recent research in biometrics and forensic analysis using gait. The experimental results affirmed the robustness of our approach to successfully detect walking people as well as its potency to extract gait features for different camera viewpoints achieving an identity recognition rate of 73.6 % processed for 2270 video sequences. Furthermore, experimental results confirmed the potential of the proposed method for identity tracking in real surveillance systems to recognize walking individuals across different views with an average recognition rate of 92.5 % for cross-camera matching for two different non-overlapping views.<br/

Who is who at different cameras: people re-identification using depth cameras

This study proposes the concept of bodyprints to perform re-identification of people in surveillance videos. Bodyprints are obtained using calibrated depth-colour cameras such as kinect. The author's results on a database of 40 people show that bodyprints are very robust to changes of pose, point of view and illumination. Potential applications include tracking people with networks of non-overlapping cameras. © 2012 The Institution of Engineering and Technology.The work presented in this paper has been funded by the Spanish Ministry of Science and Technology under the CICYT contract TEVISMART, TEC2009-09146.Albiol Colomer, AJ.; Albiol Colomer, A.; Oliver Moll, J.; Mossi García, JM. (2012). Who is who at different cameras: people re-identification using depth cameras. IET Computer Vision. 6(5):378-387. https://doi.org/10.1049/iet-cvi.2011.0140S37838765Dee, H. M., & Velastin, S. A. (2007). How close are we to solving the problem of automated visual surveillance? Machine Vision and Applications, 19(5-6), 329-343. doi:10.1007/s00138-007-0077-zhttp://www.pointclouds.org/Zhang, Z., & Troje, N. F. (2005). View-independent person identification from human gait. Neurocomputing, 69(1-3), 250-256. doi:10.1016/j.neucom.2005.06.002Bazzani, L., Cristani, M., Perina, A., Farenzena, M., & Murino, V. (2010). Multiple-Shot Person Re-identification by HPE Signature. 2010 20th International Conference on Pattern Recognition. doi:10.1109/icpr.2010.349Doretto, G., Sebastian, T., Tu, P., & Rittscher, J. (2011). Appearance-based person reidentification in camera networks: problem overview and current approaches. Journal of Ambient Intelligence and Humanized Computing, 2(2), 127-151. doi:10.1007/s12652-010-0034-yBk, S., Corvee, E., Bremond, F., & Thonnat, M. (2010). Person Re-identification Using Spatial Covariance Regions of Human Body Parts. 2010 7th IEEE International Conference on Advanced Video and Signal Based Surveillance. doi:10.1109/avss.2010.34Da-Jinn Wang, Chao-Ho Chen, Tsong-Yi Chen, & Chien-Tsung Lee. (2009). People Recognition for Entering &#x00026; Leaving a Video Surveillance Area. 2009 Fourth International Conference on Innovative Computing, Information and Control (ICICIC). doi:10.1109/icicic.2009.293Bird, N. D., Masoud, O., Papanikolopoulos, N. P., & Isaacs, A. (2005). Detection of Loitering Individuals in Public Transportation Areas. IEEE Transactions on Intelligent Transportation Systems, 6(2), 167-177. doi:10.1109/tits.2005.848370Oliveira, I. O. de, & Pio, J. L. de S. (2009). People Reidentification in a Camera Network. 2009 Eighth IEEE International Conference on Dependable, Autonomic and Secure Computing. doi:10.1109/dasc.2009.33Hamdoun, O., Moutarde, F., Stanciulescu, B., & Steux, B. (2008). Person re-identification in multi-camera system by signature based on interest point descriptors collected on short video sequences. 2008 Second ACM/IEEE International Conference on Distributed Smart Cameras. doi:10.1109/icdsc.2008.4635689Office, U.H.: ‘i-LIDS multiple camera tracking scenario definition’, 2008)http://www.gpiv.upv.es/kinect_data/http://www.primesense.com/http://www.openni.org/http://opencv.willowgarage.com/http://www.ros.org/http://kinectforwindows.org/Grimaud, M. (1992). New measure of contrast: the dynamics. Image Algebra and Morphological Image Processing III. doi:10.1117/12.60650Beucher, S., and Meyer, F.: ‘The morphological approach to segmentation: the watershed transformation’, (Marcel-Dekker 1992), p. 433–4

Review of Person Re-identification Techniques

Person re-identification across different surveillance cameras with disjoint fields of view has become one of the most interesting and challenging subjects in the area of intelligent video surveillance. Although several methods have been developed and proposed, certain limitations and unresolved issues remain. In all of the existing re-identification approaches, feature vectors are extracted from segmented still images or video frames. Different similarity or dissimilarity measures have been applied to these vectors. Some methods have used simple constant metrics, whereas others have utilised models to obtain optimised metrics. Some have created models based on local colour or texture information, and others have built models based on the gait of people. In general, the main objective of all these approaches is to achieve a higher-accuracy rate and lowercomputational costs. This study summarises several developments in recent literature and discusses the various available methods used in person re-identification. Specifically, their advantages and disadvantages are mentioned and compared.Comment: Published 201

Extending quality and covariate analyses for gait biometrics

Recognising humans by the way they walk has attracted a significant interest in recent years due to its potential use in a number of applications such as automated visual surveillance. Technologies utilising gait biometrics have the potential to provide safer society and improve quality of life. However, automated gait recognition is a very challenging research problem and some fundamental issues remain unsolved.At the moment, gait recognition performs well only when samples acquired in similar conditions are matched. An operational automated gait recognition system does not yet exist. The primary aim of the research presented in this thesis is to understand the main challenges associated with deployment of gait recognition and to propose novel solutions to some of the most fundamental issues. There has been lack of understanding of the effect of some subject dependent covariates on gait recognition performance. We have proposed a novel dataset that allows analyses of various covariates in a principled manner. The results of the database evaluation revealed that elapsed time does not affect recognition in the short to medium term, contrary to what other studies have concluded. The analyses show how other factors related to the subject affect recognition performance.Only few gait recognition approaches have been validated in real world conditions. We have collected a new dataset at two realistic locations. Using the database we have shown that there are many environment related factors that can affect performance. The quality of silhouettes has been identified as one of the most important issues for translating gait recognition research to the ‘real-world’. The existing quality algorithms proved insufficient and therefore we extended quality metrics and proposed new ways of improving signature quality and therefore performance. A new fully working automated system has been implemented.Experiments using the system in ‘real-world’ conditions have revealed additional challenges not present when analysing datasets of fixed size. In conclusion, the research has investigated many of the factors that affect current gait recognition algorithms and has presented novel approaches of dealing with some of the most important issues related to translating gait recognition to real-world environments