1. What have the authors contributed in "Finding paths in video sequences" ?
This paper investigates the task of identifying frequently-used pathways from video sequences of natural outdoor scenes.
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Finding paths in video sequences
Dimitrios Makris,Tim Ellis +1 more
- 01 Jul 2001
pp 1-10
TL;DR: This paper investigates the practice of identifying frequently-used pathways from video sequences of natural outdoor scenes by adapting path models adaptively learnt from the accumulation of trajectory data over many image frames.
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Abstract: This paper investigates the ta sk of identifying frequently-used pathways from video sequences of natural outdoor scenes. Path models are adaptively learnt from the accumulation of trajectory data over many image frames. Labelled paths are used as an efficient means for compressing the trajectory data for logging purposes. In addition, the path models are used to predict the object’s location many timesteps ahead, and to aid the recognition of unusual behaviour identified as atypical object motion.
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Figures
Table 1. Nine main routes extracted from sequence. 
Figure 3. a) Route 7, b) routes 4 and 5, c) ‘mean’ trajectory of all major routes. 
Figure 1a. Sample trajectories plotted in image plane and b) motion histogram image. 
Figure 4. a-c) Classification of trajectories using route model. d) un-classified routes not recognised by the system. 
Figure 5a. Detected junction (red circles) Figure 5b. Green rectangles indicate the and entry-exit points (blue circles). merged junctions and entry-exits. 
Figure 2. Spline and extrema representation of a route
Citations
Learning semantic scene models from observing activity in visual surveillance
Dimitrios Makris,Tim Ellis +1 more
- 01 Jun 2005
TL;DR: This paper considers the problem of automatically learning an activity-based semantic scene model from a stream of video data and proposes a scene model that labels regions according to an identifiable activity in each region, such as entry/exit zones, junctions, paths, and stop zones.
Incremental Learning of Statistical Motion Patterns With Growing Hidden Markov Models
TL;DR: This work presents an approach where motion patterns can be learned incrementally and in parallel with prediction, based on a novel extension to hidden Markov models (HMMs) - called growing hidden MarkOV models - which gives the ability to incrementally learn both the parameters and the structure of the model.
Combining image regions and human activity for indirect object recognition in indoor wide-angle views
Patrick Peursum,Geoff West,Svetha Venkatesh +2 more
- 17 Oct 2005
TL;DR: A Bayesian network is implemented for classifying region patches with object labels, building upon the previous work in automatically segmenting and recognising a human's interactions with the objects.
Partial Observation vs. Blind Tracking through Occlusion
TL;DR: This paper presents a framework for multi-objectacking from a single fixed camera, where the region-based representations of each object are tracked and redicted using a Kalman filter and a scene model is created to help predict and interpret the occluded or exiting objects.
Learning to Track Objects Through Unobserved Regions
Chris Stauffer
- 05 Jan 2005
TL;DR: This paper investigates an unsupervised hypothesis testing method for learning the characteristics of objects passing unobserved from one observed location to another that is robust to non-stationary traffic processes that result from traffic lights, vehicle grouping, and other non-linear vehicle-vehicle interactions.
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