Traffic-Aware Multi-Camera Tracking of Vehicles Based on ReID and Camera Link Model

Multi-target multi-camera tracking (MTMCT), i.e., tracking multiple targets across multiple cameras, is a crucial technique for smart city applications. In this paper, we propose an effective and reliable MTMCT framework for vehicles, which consists of a traffic-aware single camera tracking (TSCT) algorithm, a trajectory-based camera link model (CLM) for vehicle re-identification (ReID), and a hierarchical clustering algorithm to obtain the cross camera vehicle trajectories. First, the TSCT, which jointly considers vehicle appearance, geometric features, and some common traffic scenarios, is proposed to track the vehicles in each camera separately. Second, the trajectory-based CLM is adopted to facilitate the relationship between each pair of adjacently connected cameras and add spatio-temporal constraints for the subsequent vehicle ReID with temporal attention. Third, the hierarchical clustering algorithm is used to merge the vehicle trajectories among all the cameras to obtain the final MTMCT results. Our proposed MTMCT is evaluated on the CityFlow dataset and achieves a new state-of-the-art performance with IDF1 of 74.93%.Comment: Accepted by ACM International Conference on Multimedia 202

3D People Surveillance on Range Data Sequences of a Rotating Lidar

In this paper, we propose an approach on real-time 3D people surveillance, with probabilistic foreground modeling, multiple person tracking and on-line re-identification. Our principal aim is to demonstrate the capabilities of a special range sensor, called rotating multi-beam (RMB) Lidar, as a future possible surveillance camera. We present methodological contributions in two key issues. First, we introduce a hybrid 2D--3D method for robust foreground-background classification of the recorded RMB-Lidar point clouds, with eliminating spurious effects resulted by quantification error of the discretized view angle, non-linear position corrections of sensor calibration, and background flickering, in particularly due to motion of vegetation. Second, we propose a real-time method for moving pedestrian detection and tracking in RMB-Lidar sequences of dense surveillance scenarios, with short- and long-term object assignment. We introduce a novel person re-identification algorithm based on solely the Lidar measurements, utilizing in parallel the range and the intensity channels of the sensor, which provide biometric features. Quantitative evaluation is performed on seven outdoor Lidar sequences containing various multi-target scenarios displaying challenging outdoor conditions with low point density and multiple occlusions

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

Discovering Discriminative Geometric Features with Self-Supervised Attention for Vehicle Re-Identification and Beyond

In the literature of vehicle re-identification (ReID), intensive manual labels such as landmarks, critical parts or semantic segmentation masks are often required to improve the performance. Such extra information helps to detect locally geometric features as a part of representation learning for vehicles. In contrast, in this paper, we aim to address the challenge of {\em automatically} learning to detect geometric features as landmarks {\em with no extra labels}. To the best of our knowledge, we are the {\em first} to successfully learn discriminative geometric features for vehicle ReID based on self-supervised attention. Specifically, we implement an end-to-end trainable deep network architecture consisting of three branches: (1) a global branch as backbone for image feature extraction, (2) an attentional branch for producing attention masks, and (3) a self-supervised branch for regularizing the attention learning with rotated images to locate geometric features. %Our network design naturally leads to an end-to-end multi-task joint optimization. We conduct comprehensive experiments on three benchmark datasets for vehicle ReID, \ie VeRi-776, CityFlow-ReID, and VehicleID, and demonstrate our state-of-the-art performance. %of our approach with the capability of capturing informative vehicle parts with no corresponding manual labels. We also show the good generalization of our approach in other ReID tasks such as person ReID and multi-target multi-camera (MTMC) vehicle tracking. {\em Our demo code is attached in the supplementary file.

Learning Discriminative Features for Person Re-Identification

For fulfilling the requirements of public safety in modern cities, more and more large-scale surveillance camera systems are deployed, resulting in an enormous amount of visual data. Automatically processing and interpreting these data promote the development and application of visual data analytic technologies. As one of the important research topics in surveillance systems, person re-identification (re-id) aims at retrieving the target person across non-overlapping camera-views that are implemented in a number of distributed space-time locations. It is a fundamental problem for many practical surveillance applications, eg, person search, cross-camera tracking, multi-camera human behavior analysis and prediction, and it received considerable attentions nowadays from both academic and industrial domains. Learning discriminative feature representation is an essential task in person re-id. Although many methodologies have been proposed, discriminative re-id feature extraction is still a challenging problem due to: (1) Intra- and inter-personal variations. The intrinsic properties of the camera deployment in surveillance system lead to various changes in person poses, view-points, illumination conditions etc. This may result in the large intra-personal variations and/or small inter-personal variations, thus incurring problems in matching person images. (2) Domain variations. The domain variations between different datasets give rise to the problem of generalization capability of re-id model. Directly applying a re-id model trained on one dataset to another one usually causes a large performance degradation. (3) Difficulties in data creation and annotation. Existing person re-id methods, especially deep re-id methods, rely mostly on a large set of inter-camera identity labelled training data, requiring a tedious data collection and annotation process. This leads to poor scalability in practical person re-id applications. Corresponding to the challenges in learning discriminative re-id features, this thesis contributes to the re-id domain by proposing three related methodologies and one new re-id setting: (1) Gaussian mixture importance estimation. Handcrafted features are usually not discriminative enough for person re-id because of noisy information, such as background clutters. To precisely evaluate the similarities between person images, the main task of distance metric learning is to filter out the noisy information. Keep It Simple and Straightforward MEtric (KISSME) is an effective method in person re-id. However, it is sensitive to the feature dimensionality and cannot capture the multi-modes in dataset. To this end, a Gaussian Mixture Importance Estimation re-id approach is proposed, which exploits the Gaussian Mixture Models for estimating the observed commonalities of similar and dissimilar person pairs in the feature space. (2) Unsupervised domain-adaptive person re-id based on pedestrian attributes. In person re-id, person identities are usually not overlapped among different domains (or datasets) and this raises the difficulties in generalizing re-id models. Different from person identity, pedestrian attributes, eg., hair length, clothes type and color, are consistent across different domains (or datasets). However, most of re-id datasets lack attribute annotations. On the other hand, in the field of pedestrian attribute recognition, there is a number of datasets labeled with attributes. Exploiting such data for re-id purpose can alleviate the shortage of attribute annotations in re-id domain and improve the generalization capability of re-id model. To this end, an unsupervised domain-adaptive re-id feature learning framework is proposed to make full use of attribute annotations. Specifically, an existing unsupervised domain adaptation method has been extended to transfer attribute-based features from attribute recognition domain to the re-id domain. With the proposed re-id feature learning framework, the domain invariant feature representations can be effectively extracted. (3) Intra-camera supervised person re-id. Annotating the large-scale re-id datasets requires a tedious data collection and annotation process and therefore leads to poor scalability in practical person re-id applications. To overcome this fundamental limitation, a new person re-id setting is considered without inter-camera identity association but only with identity labels independently annotated within each camera-view. This eliminates the most time-consuming and tedious inter-camera identity association annotating process and thus significantly reduces the amount of human efforts required during annotation. It hence gives rise to a more scalable and more feasible learning scenario, which is named as Intra-Camera Supervised (ICS) person re-id. Under this ICS setting, a new re-id method, i.e., Multi-task Mulit-label (MATE) learning method, is formulated. Given no inter-camera association, MATE is specially designed for self-discovering the inter-camera identity correspondence. This is achieved by inter-camera multi-label learning under a joint multi-task inference framework. In addition, MATE can also efficiently learn the discriminative re-id feature representations using the available identity labels within each camera-view

Object Tracking: Appearance Modeling And Feature Learning

Object tracking in real scenes is an important problem in computer vision due to increasing usage of tracking systems day in and day out in various applications such as surveillance, security, monitoring and robotic vision. Object tracking is the process of locating objects of interest in every frame of video frames. Many systems have been proposed to address the tracking problem where the major challenges come from handling appearance variation during tracking caused by changing scale, pose, rotation, illumination and occlusion. In this dissertation, we address these challenges by introducing several novel tracking techniques. First, we developed a multiple object tracking system that deals specially with occlusion issues. The system depends on our improved KLT tracker for accurate and robust tracking during partial occlusion. In full occlusion, we applied a Kalman filter to predict the object\u27s new location and connect the trajectory parts. Many tracking methods depend on a rectangle or an ellipse mask to segment and track objects. Typically, using a larger or smaller mask will lead to loss of tracked objects. Second, we present an object tracking system (SegTrack) that deals with partial and full occlusions by employing improved segmentation methods: mixture of Gaussians and a silhouette segmentation algorithm. For re-identification, one or more feature vectors for each tracked object are used after target reappearing. Third, we propose a novel Bayesian Hierarchical Appearance Model (BHAM) for robust object tracking. Our idea is to model the appearance of a target as combination of multiple appearance models, each covering the target appearance changes under a certain situation (e.g. view angle). In addition, we built an object tracking system by integrating BHAM with background subtraction and the KLT tracker for static camera videos. For moving camera videos, we applied BHAM to cluster negative and positive target instances. As tracking accuracy depends mainly on finding good discriminative features to estimate the target location, finally, we propose to learn good features for generic object tracking using online convolutional neural networks (OCNN). In order to learn discriminative and stable features for tracking, we propose a novel object function to train OCNN by penalizing the feature variations in consecutive frames, and the tracker is built by integrating OCNN with a color-based multi-appearance model. Our experimental results on real-world videos show that our tracking systems have superior performance when compared with several state-of-the-art trackers. In the feature, we plan to apply the Bayesian Hierarchical Appearance Model (BHAM) for multiple objects tracking

A multi-viewpoint feature-based re-identification system driven by skeleton keypoints

Thanks to the increasing popularity of 3D sensors, robotic vision has experienced huge improvements in a wide range of applications and systems in the last years. Besides the many benefits, this migration caused some incompatibilities with those systems that cannot be based on range sensors, like intelligent video surveillance systems, since the two kinds of sensor data lead to different representations of people and objects. This work goes in the direction of bridging the gap, and presents a novel re-identification system that takes advantage of multiple video flows in order to enhance the performance of a skeletal tracking algorithm, which is in turn exploited for driving the re-identification. A new, geometry-based method for joining together the detections provided by the skeletal tracker from multiple video flows is introduced, which is capable of dealing with many people in the scene, coping with the errors introduced in each view by the skeletal tracker. Such method has a high degree of generality, and can be applied to any kind of body pose estimation algorithm. The system was tested on a public dataset for video surveillance applications, demonstrating the improvements achieved by the multi-viewpoint approach in the accuracy of both body pose estimation and re-identification. The proposed approach was also compared with a skeletal tracking system working on 3D data: the comparison assessed the good performance level of the multi-viewpoint approach. This means that the lack of the rich information provided by 3D sensors can be compensated by the availability of more than one viewpoint