IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part I
Learning Patterns of Activity Using Real-Time Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Using Adaptive Tracking to Classify and Monitor Activities in a Site
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Multi Feature Path Modeling for Video Surveillance
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Multi-Object Tracking Through Simultaneous Long Occlusions and Split-Merge Conditions
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
A System for Learning Statistical Motion Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recognition and Segmentation of Scene Content using Region-Based Classification
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 01
Coupled Hidden Semi Markov Models for Activity Recognition
WMVC '07 Proceedings of the IEEE Workshop on Motion and Video Computing
Learning Motion Patterns in Surveillance Video using HMM Clustering
WMVC '08 Proceedings of the 2008 IEEE Workshop on Motion and video Computing
Learning semantic scene models from observing activity in visual surveillance
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Content-based retrieval of functional objects in video using scene context
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
Unsupervised learning of functional categories in video scenes
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part II
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part II
A review of motion analysis methods for human Nonverbal Communication Computing
Image and Vision Computing
| Hi-index | 0.00 |
We present a method to detect and recognize functional scene elements in video scenes. A functional scene element is a location or object that is primarily defined by its specific function or purpose, rather than its appearance or shape. Our method combines techniques from video scene analysis with functional recognition to decompose a video scene into its functional elements such as parking spots, building entrances, roads and sidewalks. Existing techniques for functional object recognition in video [2,3] are designed for high-resolution video with little clutter and constrained situations, while our approach is designed for real-world video surveillance scenes where there are many movers, and detection and tracking can be poor because of low resolution and frame rates. Video scene analysis methods are focused on motion pattern learning and anomaly detection [4][8][11][12][13][14], whereas we take a recognition approach and develop motion pattern models for specific functional categories. The movements of objects such as vehicles and pedestrians are exploited to detect and classify functional scene elements in an online process that probabilistically accumulates evidence over many tracks to compensate for noisy and partial observations. Results are shown on simulated and real data of complex, busy scenes containing multiple instances of different functional objects. The detected elements are then used to demonstrate that building activity profiles can be extracted and used to distinguish different types of buildings.