IEEE Transactions on Pattern Analysis and Machine Intelligence
Pedestrian Detection Using Wavelet Templates
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Moment invariants for recognition under changing viewpoint and illumination
Computer Vision and Image Understanding - Special issue on color for image indexing and retrieval
Detecting Pedestrians Using Patterns of Motion and Appearance
International Journal of Computer Vision
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
A Performance Evaluation of Local Descriptors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature-Based Sequence-to-Sequence Matching
International Journal of Computer Vision
Person Reidentification Using Spatiotemporal Appearance
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Multicamera People Tracking with a Probabilistic Occupancy Map
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pedestrian Detection via Classification on Riemannian Manifolds
IEEE Transactions on Pattern Analysis and Machine Intelligence
Object detection and matching in a mixed network of fixed and mobile cameras
AREA '08 Proceedings of the 1st ACM workshop on Analysis and retrieval of events/actions and workflows in video streams
Viewpoint Invariant Pedestrian Recognition with an Ensemble of Localized Features
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Tracking Multiple Occluding People by Localizing on Multiple Scene Planes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multi-scale phase-based local features
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
SURF: speeded up robust features
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Region covariance: a fast descriptor for detection and classification
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
Semi-interactive tracing of persons in real-life surveillance data
Proceedings of the 2nd ACM workshop on Multimedia in forensics, security and intelligence
Color based tracing in real-life surveillance data
Transactions on data hiding and multimedia security V
Sparsity Driven People Localization with a Heterogeneous Network of Cameras
Journal of Mathematical Imaging and Vision
SARC3D: a new 3D body model for people tracking and re-identification
ICIAP'11 Proceedings of the 16th international conference on Image analysis and processing: Part I
3DPeS: 3D people dataset for surveillance and forensics
J-HGBU '11 Proceedings of the 2011 joint ACM workshop on Human gesture and behavior understanding
Track based relevance feedback for tracing persons in surveillance videos
Computer Vision and Image Understanding
People reidentification in surveillance and forensics: A survey
ACM Computing Surveys (CSUR)
On-the-fly feature importance mining for person re-identification
Pattern Recognition
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Most multi-camera systems assume a well structured environment to detect and track objects across cameras. Cameras need to be fixed and calibrated, or only objects within a training data can be detected (e.g. pedestrians only). In this work, a master-slave system is presented to detect and track any objects in a network of uncalibrated fixed and mobile cameras. Cameras can have non-overlapping field-of-views. Objects are detected with the mobile cameras (the slaves) given only observations from the fixed cameras (the masters). No training stage and data are used. Detected objects are correctly tracked across cameras leading to a better understanding of the scene. A cascade of grids of region descriptors is proposed to describe any object of interest. To lend insight on the addressed problem, most state-of-the-art region descriptors are evaluated given various schemes. The covariance matrix of various features, the histogram of colors, the histogram of oriented gradients, the scale invariant feature transform (SIFT), the speeded-up robust features (SURF) descriptors, and the color interest points [1] are evaluated. A sparse scan of the cameras'image plane is also presented to reduce the search space of the localization process, approaching nearly real-time performance. The proposed approach outperforms existing works such as scale invariant feature transform (SIFT), or the speeded-up robust features (SURF). The approach is robust to some changes in illumination, viewpoint, color distribution, image quality, and object deformation. Objects with partial occlusion are also detected and tracked.