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

Robust perception of humans for mobile robots RGB-depth algorithms for people tracking, re-identification and action recognition

Human perception is one of the most important skills for a mobile robot sharing its workspace with humans. This is not only true for navigation, because people have to be avoided differently than other obstacles, but also because mobile robots must be able to truly interact with humans. In a near future, we can imagine that robots will be more and more present in every house and will perform services useful to the well-being of humans. For this purpose, robust people tracking algorithms must be exploited and person re-identification techniques play an important role for allowing robots to recognize a person after a full occlusion or after long periods of time. Moreover, they must be able to recognize what humans are doing, in order to react accordingly, helping them if needed or also learning from them. This thesis tackles these problems by proposing approaches which combine algorithms based on both RGB and depth information which can be obtained with recently introduced consumer RGB-D sensors. Our key contribution to people detection and tracking research is a depth-clustering method which allows to apply a robust image-based people detector only to a small subset of possible detection windows, thus decreasing the number of false detections while reaching high computational efficiency. We also advance person re-identification research by proposing two techniques exploiting depth-based skeletal tracking algorithms: one is targeted to short-term re-identification and creates a compact, yet discrimative signature of people based on computing features at skeleton keypoints, which are highly repeatable and semantically meaningful; the other extract long-term features, such as 3D shape, to compare people by matching the corresponding 3D point cloud acquired with a RGB-D sensor. In order to account for the fact that people are articulated and not rigid objects, it exploits 3D skeleton information for warping people point clouds to a standard pose, thus making them directly comparable by means of least square fitting. Finally, we describe an extension of flow-based action recognition methods to the RGB-D domain which computes motion over time of persons' 3D points by exploiting joint color and depth information and recognizes human actions by classifying gridded descriptors of 3D flow. A further contribution of this thesis is the creation of a number of new RGB-D datasets which allow to compare different algorithms on data acquired by consumer RGB-D sensors. All these datasets have been publically released in order to foster research in these fields

Recurrent Attention Models for Depth-Based Person Identification

We present an attention-based model that reasons on human body shape and motion dynamics to identify individuals in the absence of RGB information, hence in the dark. Our approach leverages unique 4D spatio-temporal signatures to address the identification problem across days. Formulated as a reinforcement learning task, our model is based on a combination of convolutional and recurrent neural networks with the goal of identifying small, discriminative regions indicative of human identity. We demonstrate that our model produces state-of-the-art results on several published datasets given only depth images. We further study the robustness of our model towards viewpoint, appearance, and volumetric changes. Finally, we share insights gleaned from interpretable 2D, 3D, and 4D visualizations of our model's spatio-temporal attention.Comment: Computer Vision and Pattern Recognition (CVPR) 201

Ensemble of Different Approaches for a Reliable Person Re-identification System

An ensemble of approaches for reliable person re-identification is proposed in this paper. The proposed ensemble is built combining widely used person re-identification systems using different color spaces and some variants of state-of-the-art approaches that are proposed in this paper. Different descriptors are tested, and both texture and color features are extracted from the images; then the different descriptors are compared using different distance measures (e.g., the Euclidean distance, angle, and the Jeffrey distance). To improve performance, a method based on skeleton detection, extracted from the depth map, is also applied when the depth map is available. The proposed ensemble is validated on three widely used datasets (CAVIAR4REID, IAS, and VIPeR), keeping the same parameter set of each approach constant across all tests to avoid overfitting and to demonstrate that the proposed system can be considered a general-purpose person re-identification system. Our experimental results show that the proposed system offers significant improvements over baseline approaches. The source code used for the approaches tested in this paper will be available at https://www.dei.unipd.it/node/2357 and http://robotics.dei.unipd.it/reid/