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 & 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

Using latent features for short-term person re-identification with RGB-D cameras

This paper presents a system for people re-identification in uncontrolled scenarios using RGB-depth cameras. Compared to conventional RGB cameras, the use of depth information greatly simplifies the tasks of segmentation and tracking. In a previous work, we proposed a similar architecture where people were characterized using color-based descriptors that we named bodyprints. In this work, we propose the use of latent feature models to extract more relevant information from the bodyprint descriptors by reducing their dimensionality. Latent features can also cope with missing data in case of occlusions. Different probabilistic latent feature models, such as probabilistic principal component analysis and factor analysis, are compared in the paper. The main difference between the models is how the observation noise is handled in each case. Re-identification experiments have been conducted in a real store where people behaved naturally. The results show that the use of the latent features significantly improves the re-identification rates compared to state-of-the-art works.The work presented in this paper has been funded by the Spanish Ministry of Science and Technology under the CICYT contract TEVISMART, TEC2009-09146.Oliver Moll, J.; Albiol Colomer, A.; Albiol Colomer, AJ.; Mossi García, JM. (2016). Using latent features for short-term person re-identification with RGB-D cameras. Pattern Analysis and Applications. 19(2):549-561. https://doi.org/10.1007/s10044-015-0489-8S549561192http://kinectforwindows.org/http://www.gpiv.upv.es/videoresearch/personindexing.htmlAlbiol A, Albiol A, Oliver J, Mossi JM (2012) Who is who at different cameras. Matching people using depth cameras. Comput Vis IET 6(5):378–387Bak S, Corvee E, Bremond F, Thonnat M (2010) Person re-identification using haar-based and dcd-based signature. In: 2nd workshop on activity monitoring by multi-camera surveillance systems, AMMCSS 2010, in conjunction with 7th IEEE international conference on advanced video and signal-based surveillance, AVSS. AVSSBak S, Corvee E, Bremond F, Thonnat M (2010) Person re-identification using spatial covariance regions of human body parts. In: Seventh IEEE international conference on advanced video and signal based surveillance. pp. 435–440Bak S, Corvee E, Bremond F, Thonnat M (2011) Multiple-shot human re-identification by mean riemannian covariance grid. In: Advanced video and signal-based surveillance. Klagenfurt, Autriche. http://hal.inria.fr/inria-00620496Baltieri D, Vezzani R, Cucchiara R, Utasi A, BenedeK C, Szirányi T (2011) Multi-view people surveillance using 3d information. In: ICCV workshops. pp. 1817–1824Barbosa BI, Cristani M, Del Bue A, Bazzani L, Murino V (2012) Re-identification with rgb-d sensors. In: First international workshop on re-identificationBasilevsky A (1994) Statistical factor analysis and related methods: theory and applications. Willey, New YorkBäuml M, Bernardin K, Fischer k, Ekenel HK, Stiefelhagen R (2010) Multi-pose face recognition for person retrieval in camera networks. In: International conference on advanced video and signal-based surveillanceBazzani L, Cristani M, Perina A, Farenzena M, Murino V (2010) Multiple-shot person re-identification by hpe signature. In: Proceedings of the 2010 20th international conference on pattern recognition. Washington, DC, USA, pp. 1413–1416Bird ND, Masoud O, Papanikolopoulos NP, Isaacs A (2005) Detection of loitering individuals in public transportation areas. IEEE Trans Intell Transp Syst 6(2):167–177Bishop CM (2006) Pattern recognition and machine learning (information science and statistics). Springer, SecaucusCha SH (2007) Comprehensive survey on distance/similarity measures between probability density functions. Int J Math Models Methods Appl Sci 1(4):300–307Cheng YM, Zhou WT, Wang Y, Zhao CH, Zhang SW (2009) Multi-camera-based object handoff using decision-level fusion. In: Conference on image and signal processing. pp. 1–5Dikmen M, Akbas E, Huang TS, Ahuja N (2010) Pedestrian recognition with a learned metric. In: Asian conference in computer visionDoretto G, Sebastian T, Tu P, Rittscher J (2011) Appearance-based person reidentification in camera networks: problem overview and current approaches. J Ambient Intell Humaniz Comput 2:1–25Farenzena M, Bazzani L, Perina A, Murino V, Cristani M (2010) Person re-identification by symmetry-driven accumulation of local features. In: Proceedings of the 2010 IEEE computer society conference on computer vision and pattern recognition (CVPR 2010). IEEE Computer Society, San Francisco, CA, USAFodor I (2002) A survey of dimension reduction techniques. Technical report. Lawrence Livermore National LaboratoryFreund Y, Iyer R, Schapire RE, Singer Y (2003) An efficient boosting algorithm for combining preferences. J Mach Learn Res 4:933–969Gandhi T, Trivedi M (2006) Panoramic appearance map (pam) for multi-camera based person re-identification. Advanced Video and Signal Based Surveillance, IEEE Conference on, p. 78Garcia J, Gardel A, Bravo I, Lazaro J (2014) Multiple view oriented matching algorithm for people reidentification. Ind Inform IEEE Trans 10(3):1841–1851Gheissari N, Sebastian TB, Hartley R (2006) Person reidentification using spatiotemporal appearance. CVPR 2:1528–1535Gray D, Brennan S, Tao H (2007) Evaluating appearance models for recognition, reacquisition, and tracking. In: Proceedings of IEEE international workshop on performance evaluation for tracking and surveillance (PETS)Gray D, Tao H (2008) Viewpoint invariant pedestrian recognition with an ensemble of localized features. In: Proceedings of the 10th european conference on computer vision: part I. Berlin, pp. 262–275 (2008)Ilin A, Raiko T (2010) Practical approaches to principal component analysis in the presence of missing values. J Mach Learn Res 99:1957–2000Javed O, Shafique O, Rasheed Z, Shah M (2008) Modeling inter-camera space–time and appearance relationships for tracking across non-overlapping views. Comput Vis Image Underst 109(2):146–162Kai J, Bodensteiner C, Arens M (2011) Person re-identification in multi-camera networks. In: Computer vision and pattern recognition workshops (CVPRW), 2011 IEEE computer society conference on, pp. 55–61Kuo CH, Huang C, Nevatia R (2010) Inter-camera association of multi-target tracks by on-line learned appearance affinity models. Proceedings of the 11th european conference on computer vision: part I, ECCV’10. Springer, Berlin, pp 383–396Lan R, Zhou Y, Tang YY, Chen C (2014) Person reidentification using quaternionic local binary pattern. In: Multimedia and expo (ICME), 2014 IEEE international conference on, pp. 1–6Loy CC, Liu C, Gong S (2013) Person re-identification by manifold ranking. In: icip. pp. 3318–3325Madden C, Cheng E, Piccardi M (2007) Tracking people across disjoint camera views by an illumination-tolerant appearance representation. Mach Vis Appl 18:233–247Mazzon R, Tahir SF, Cavallaro A (2012) Person re-identification in crowd. Pattern Recogn Lett 33(14):1828–1837Oliveira IO, Souza Pio JL (2009) People reidentification in a camera network. In: Eighth IEEE international conference on dependable, autonomic and secure computing. pp. 461–466Papadakis P, Pratikakis I, Theoharis T, Perantonis SJ (2010) Panorama: a 3d shape descriptor based on panoramic views for unsupervised 3d object retrieval. Int J Comput Vis 89(2–3):177–192Prosser B, Zheng WS, Gong S, Xiang T (2010) Person re-identification by support vector ranking. In: Proceedings of the British machine vision conference. BMVA Press, pp. 21.1–21.11Roweis S (1998) Em algorithms for pca and spca. In: Advances in neural information processing systems. MIT Press, Cambridge, pp. 626–632 (1998)Pedagadi S, Orwell J, Velastin S, Boghossian B (2013) Local fisher discriminant analysis for pedestrian re-identification. In: CVPR. pp. 3318–3325Satta R, Fumera G, Roli F (2012) Fast person re-identification based on dissimilarity representations. Pattern Recogn Lett, Special Issue on Novel Pattern Recognition-Based Methods for Reidentification in Biometric Context 33:1838–1848Tao D, Jin L, Wang Y, Li X (2015) Person reidentification by minimum classification error-based kiss metric learning. Cybern IEEE Trans 45(2):242–252Tipping ME, Bishop CM (1999) Probabilistic principal component analysis. J R Stat Soc Ser B 61:611–622Tisse CL, Martin L, Torres L, Robert M (2002) Person identification technique using human iris recognition. In: Proceedings of vision interface, pp 294–299Vandergheynst P, Bierlaire M, Kunt M, Alahi A (2009) Cascade of descriptors to detect and track objects across any network of cameras. Comput Vis Image Underst, pp 1413–1416Verbeek J (2009) Notes on probabilistic pca with missing values. Technical reportWang D, Chen CO, Chen TY, Lee CT (2009) People recognition for entering and leaving a video surveillance area. In: Fourth international conference on innovative computing, information and control. pp. 334–337Zhang Z, Troje NF (2005) View-independent person identification from human gait. Neurocomputing 69:250–256Zhao T, Aggarwal M, Kumar R, Sawhney H (2005) Real-time wide area multi-camera stereo tracking. In: IEEE computer society conference on computer vision and pattern recognition. pp. 976–983Zheng S, Xie B, Huang K, Tao D (2011) Multi-view pedestrian recognition using shared dictionary learning with group sparsity. In: Lu BL, Zhang L, Kwok JT (eds) ICONIP (3), Lecture notes in computer science, vol 7064. Springer, New York, pp. 629–638Zheng WS, Gong S, Xiang T (2011) Person re-identification by probabilistic relative distance comparison. In: Computer vision and pattern recognition (CVPR), 2011 IEEE conference on. pp. 649–65

Exploring Shape Embedding for Cloth-Changing Person Re-Identification via 2D-3D Correspondences

Cloth-Changing Person Re-Identification (CC-ReID) is a common and realistic problem since fashion constantly changes over time and people's aesthetic preferences are not set in stone. While most existing cloth-changing ReID methods focus on learning cloth-agnostic identity representations from coarse semantic cues (e.g. silhouettes and part segmentation maps), they neglect the continuous shape distributions at the pixel level. In this paper, we propose Continuous Surface Correspondence Learning (CSCL), a new shape embedding paradigm for cloth-changing ReID. CSCL establishes continuous correspondences between a 2D image plane and a canonical 3D body surface via pixel-to-vertex classification, which naturally aligns a person image to the surface of a 3D human model and simultaneously obtains pixel-wise surface embeddings. We further extract fine-grained shape features from the learned surface embeddings and then integrate them with global RGB features via a carefully designed cross-modality fusion module. The shape embedding paradigm based on 2D-3D correspondences remarkably enhances the model's global understanding of human body shape. To promote the study of ReID under clothing change, we construct 3D Dense Persons (DP3D), which is the first large-scale cloth-changing ReID dataset that provides densely annotated 2D-3D correspondences and a precise 3D mesh for each person image, while containing diverse cloth-changing cases over all four seasons. Experiments on both cloth-changing and cloth-consistent ReID benchmarks validate the effectiveness of our method.Comment: Accepted by ACM MM 202

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

Object detection, recognition and re-identification in video footage

There has been a significant number of security concerns in recent times; as a result, security cameras have been installed to monitor activities and to prevent crimes in most public places. These analysis are done either through video analytic or forensic analysis operations on human observations. To this end, within the research context of this thesis, a proactive machine vision based military recognition system has been developed to help monitor activities in the military environment. The proposed object detection, recognition and re-identification systems have been presented in this thesis. A novel technique for military personnel recognition is presented in this thesis. Initially the detected camouflaged personnel are segmented using a grabcut segmentation algorithm. Since in general a camouflaged personnel's uniform appears to be similar both at the top and the bottom of the body, an image patch is initially extracted from the segmented foreground image and used as the region of interest. Subsequently the colour and texture features are extracted from each patch and used for classification. A second approach for personnel recognition is proposed through the recognition of the badge on the cap of a military person. A feature matching metric based on the extracted Speed Up Robust Features (SURF) from the badge on a personnel's cap enabled the recognition of the personnel's arm of service. A state-of-the-art technique for recognising vehicle types irrespective of their view angle is also presented in this thesis. Vehicles are initially detected and segmented using a Gaussian Mixture Model (GMM) based foreground/background segmentation algorithm. A Canny Edge Detection (CED) stage, followed by morphological operations are used as pre-processing stage to help enhance foreground vehicular object detection and segmentation. Subsequently, Region, Histogram Oriented Gradient (HOG) and Local Binary Pattern (LBP) features are extracted from the refined foreground vehicle object and used as features for vehicle type recognition. Two different datasets with variant views of front/rear and angle are used and combined for testing the proposed technique. For night-time video analytics and forensics, the thesis presents a novel approach to pedestrian detection and vehicle type recognition. A novel feature acquisition technique named, CENTROG, is proposed for pedestrian detection and vehicle type recognition in this thesis. Thermal images containing pedestrians and vehicular objects are used to analyse the performance of the proposed algorithms. The video is initially segmented using a GMM based foreground object segmentation algorithm. A CED based pre-processing step is used to enhance segmentation accuracy prior using Census Transforms for initial feature extraction. HOG features are then extracted from the Census transformed images and used for detection and recognition respectively of human and vehicular objects in thermal images. Finally, a novel technique for people re-identification is proposed in this thesis based on using low-level colour features and mid-level attributes. The low-level colour histogram bin values were normalised to 0 and 1. A publicly available dataset (VIPeR) and a self constructed dataset have been used in the experiments conducted with 7 clothing attributes and low-level colour histogram features. These 7 attributes are detected using features extracted from 5 different regions of a detected human object using an SVM classifier. The low-level colour features were extracted from the regions of a detected human object. These 5 regions are obtained by human object segmentation and subsequent body part sub-division. People are re-identified by computing the Euclidean distance between a probe and the gallery image sets. The experiments conducted using SVM classifier and Euclidean distance has proven that the proposed techniques attained all of the aforementioned goals. The colour and texture features proposed for camouflage military personnel recognition surpasses the state-of-the-art methods. Similarly, experiments prove that combining features performed best when recognising vehicles in different views subsequent to initial training based on multi-views. In the same vein, the proposed CENTROG technique performed better than the state-of-the-art CENTRIST technique for both pedestrian detection and vehicle type recognition at night-time using thermal images. Finally, we show that the proposed 7 mid-level attributes and the low-level features results in improved performance accuracy for people re-identification