Partial Observation vs. Blind Tracking through Occlusion

Xu, Ming; Ellis, Tim

doi:10.5244/c.16.76

Cited by 45 publications

(25 citation statements)

References 4 publications

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“…Borrowing terminology from Ellis & Xu [9], we can expect trajectories to end in a number of different types of situation, or "occlusion": Short Term occlusions, where we can expect the agent to reappear (like a hedge) and other cases (Border occlusions and Long Term occlusions) where the agent has actually left the scene. For the purposes of this study, we treat all three of their cases identically and do not try to unify trajectories in situations with short term occlusions.…”

Section: The Exit Modelmentioning

confidence: 99%

Detecting inexplicable behaviour

Dee¹,

Hogg²

2004

Procedings of the British Machine Vision Conference 2004

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This paper presents a novel approach to the detection of unusual or interesting events in videos involving certain types of intentional behaviour, such as pedestrian scenes. The approach is not based upon a statistical measure of typicality, but upon building an understanding of the way people navigate towards a goal. The activity of agents moving around within the scene is evaluated based upon whether the behaviour in question is consistent with a simple model of goal-directed behaviour and a model of those goals and obstacles known to be in the scene. The advantages of such an approach are multiple: it handles the presence of movable obstacles (for example, parked cars) with ease; trajectories which have never before been presented to the system can be classified as explicable; and the technique as a whole has a prima facie psychological plausibility. A system based upon these principles is demonstrated in two scenes: a car-park, and in a foyer scenario 1 .

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Section: The Exit Modelmentioning

confidence: 99%

Detecting inexplicable behaviour

Dee¹,

Hogg²

2004

Procedings of the British Machine Vision Conference 2004

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“…The tracking method described above is an effective and robust method for dealing with partial occlusions when the bounding box of two objects are overlapped in part, and further details on the procedure for splitting two merged objects can be found in [31]. However, if the bounding box of one (smaller) object, such as the ball, is completely contained by another (larger) object's bounding box, then there is no valid observation for the ball, and the estimated state will be updated on prediction only.…”

Section: Tracking Correctionmentioning

confidence: 99%

Tracking the soccer ball using multiple fixed cameras

Ren

Orwell

Jones

et al. 2009

Computer Vision and Image Understanding

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“…In 8 this Section, separating each group of players, in particular groups of two, are discussed by assuming that each target has a slowly varying height and width [22].…”

Section: Filtering and Categorizing Of Playersmentioning

confidence: 99%

Multi-camera video surveillance for real-time analysis and reconstruction of soccer games

Ren

Orwell

et al. 2009

Machine Vision and Applications

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Partial Observation vs. Blind Tracking through Occlusion

Cited by 45 publications

References 4 publications

Detecting inexplicable behaviour

Detecting inexplicable behaviour

Tracking the soccer ball using multiple fixed cameras

Multi-camera video surveillance for real-time analysis and reconstruction of soccer games

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