W4: Real-Time Surveillance of People and Their Activities
IEEE Transactions on Pattern Analysis and Machine Intelligence
A survey of computer vision-based human motion capture
Computer Vision and Image Understanding - Modeling people toward vision-based underatanding of a person's shape, appearance, and movement
Global and local deformations of solid primitives
SIGGRAPH '84 Proceedings of the 11th annual conference on Computer graphics and interactive techniques
Real-Time Self-Calibrating Stereo Person Tracking Using 3-D Shape Estimation from Blob Features
ICPR '96 Proceedings of the International Conference on Pattern Recognition (ICPR '96) Volume III-Volume 7276 - Volume 7276
Representation and description of curved objects
Representation and description of curved objects
Detecting Pedestrians Using Patterns of Motion and Appearance
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Pedestrian Detection in Crowded Scenes
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
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
Fusion of Multi-View Silhouette Cues Using a Space Occupancy Grid
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Putting Objects in Perspective
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
ACM Computing Surveys (CSUR)
A survey of advances in vision-based human motion capture and analysis
Computer Vision and Image Understanding - Special issue on modeling people: Vision-based understanding of a person's shape, appearance, movement, and behaviour
International Journal of Computer Vision
Multicamera People Tracking with a Probabilistic Occupancy Map
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Object Detection with Interleaved Categorization and Segmentation
International Journal of Computer Vision
3D Urban Scene Modeling Integrating Recognition and Reconstruction
International Journal of Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Tracking Multiple Occluding People by Localizing on Multiple Scene Planes
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Semi-automatic System for Ground Truth Generation of Soccer Video Sequences
AVSS '09 Proceedings of the 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance
Monocular Pedestrian Detection: Survey and Experiments
IEEE Transactions on Pattern Analysis and Machine Intelligence
Monocular 3D scene modeling and inference: understanding multi-object traffic scenes
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
A multiview approach to tracking people in crowded scenes using a planar homography constraint
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
The estimation of the gradient of a density function, with applications in pattern recognition
IEEE Transactions on Information Theory
Birdlets: Subordinate categorization using volumetric primitives and pose-normalized appearance
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Assessing team strategy using spatiotemporal data
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Hybrid robotic/virtual pan-tilt-zom cameras for autonomous event recording
Proceedings of the 21st ACM international conference on Multimedia
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Multiview object detection methods achieve robustness in adverse imaging conditions by exploiting projective consistency across views. In this paper, we present an algorithm that achieves performance comparable to multiview methods from a single camera by employing geometric primitives as proxies for the true 3D shape of objects, such as pedestrians or vehicles. Our key insight is that for a calibrated camera, geometric primitives produce predetermined location-specific patterns in occupancy maps. We use these to define spatially-varying kernel functions of projected shape. This leads to an analytical formation model of occupancy maps as the convolution of locations and projected shape kernels. We estimate object locations by deconvolving the occupancy map using an efficient template similarity scheme. The number of objects and their positions are determined using the mean shift algorithm. The approach is highly parallel because the occupancy probability of a particular geometric primitive at each ground location is an independent computation. The algorithm extends to multiple cameras without requiring significant bandwidth. We demonstrate comparable performance to multiview methods and show robust, realtime object detection on full resolution HD video in a variety of challenging imaging conditions.