Learning Patterns of Activity Using Real-Time Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast Approximate Energy Minimization via Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Generation of Semantic Regions from Image Sequences
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume II - Volume II
Recognizing Action at a Distance
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
What Energy Functions Can Be Minimizedvia Graph Cuts?
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Detecting Motion Patterns via Direction Maps with Application to Surveillance
WACV-MOTION '05 Proceedings of the Seventh IEEE Workshops on Application of Computer Vision (WACV/MOTION'05) - Volume 1 - Volume 01
Summarising contextual activity and detecting unusual inactivity in a supportive home environment
Pattern Analysis & Applications
International Journal of Computer Vision
Beyond Tracking: Modelling Activity and Understanding Behaviour
International Journal of Computer Vision
An iterative image registration technique with an application to stereo vision
IJCAI'81 Proceedings of the 7th international joint conference on Artificial intelligence - Volume 2
Unsupervised scene analysis: A hidden Markov model approach
Computer Vision and Image Understanding
Human detection using oriented histograms of flow and appearance
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
Learning semantic scene models from observing activity in visual surveillance
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Building semantic scene models from unconstrained video
Computer Vision and Image Understanding
| Hi-index | 0.00 |
This paper describes a method for building visual "maps" from video data using quantized descriptions of motion. This enables unsupervised classification of scene regions based upon the motion patterns observed within them. Our aim is to recognise generic places using a qualitative representation of the spatial layout of regions with common motion patterns. Such places are characterised by the distribution of these motion patterns as opposed to static appearance patterns, and could include locations such as train platforms, bus stops, and park benches. Motion descriptions are obtained by tracking image features over a temporal window, and are then subjected to normalisation and thresholding to provide a quantized representation of that feature's gross motion. Input video is quantized spatially into N×Npixel blocks, and a histogram of the frequency of occurrence of each vector is then built for each of these small areas of scene. Within these we can therefore characterise the dominant patterns of motion, and then group our spatial regions based upon both proximity and local motion similarity to define areas or regions with particular motion characteristics. Moving up a level we then consider the relationship between the motion in adjacent spatial areas, and can characterise the dominant patterns of motion expected in a particular part of the scene over time. The current paper differs from previous work which has largely been based on the pathsof moving agents, and therefore restricted to scenes in which such paths are identifiable. We demonstrate our method in three very different scenes: an indoor room scenario with multiple chairs and unpredictable unconstrained motion, an underground station featuring regions where motion is constrained (train tracks) and regions with complicated motion and difficult occlusion relationships (platform), and an outdoor scene with challenging camera motion and partially overlapping video streams.