Tracking multiple vehicles using foreground, background and motion models

Magee, Derek R.

doi:10.1016/s0262-8856(03)00145-8

Cited by 193 publications

(97 citation statements)

References 7 publications

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“…The groundplane homography was established using the ground truth measurements provided as part of the dataset. Moving objects were detected and tracked using a generic tracker [14] to retrieve foreground segmentations. The tracker has an automatic shadow remover that worked efficiently on the dataset.…”

Section: Experiments and Resultsmentioning

confidence: 99%

Detecting Carried Objects in Short Video Sequences

Damen

Hogg

2008

Lecture Notes in Computer Science

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Abstract. We propose a new method for detecting objects such as bags carried by pedestrians depicted in short video sequences. In common with earlier work [1,2] on the same problem, the method starts by averaging aligned foreground regions of a walking pedestrian to produce a representation of motion and shape (known as a temporal template)t h a t has some immunity to noise in foreground segmentations and phase of the walking cycle. Our key novelty is for carried objects to be revealed by comparing the temporal templates against view-specific exemplars generated offline for unencumbered pedestrians. A likelihood map obtained from this match is combined in a Markov random field with a map of prior probabilities for carried objects and a spatial continuity assumption, from which we obtain a segmentation of carried objects using the MAP solution. We have re-implemented the earlier state of the art method [1] and demonstrate a substantial improvement in performance for the new method on the challenging PETS2006 dataset [3]. Although developed for a specific problem, the method could be applied to the detection of irregularities in appearance for other categories of object that move in a periodic fashion.

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Section: Experiments and Resultsmentioning

confidence: 99%

Detecting Carried Objects in Short Video Sequences

Damen

Hogg

2008

Lecture Notes in Computer Science

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“…Most extant approaches to classifying vehicular traffic videos use a combination of segmentation and tracking, e.g., [24,25,3,28,35,11,23,22,30,29,6]. The general procedure consists of the following three steps: (i) motion detection, (ii) tracking of the individual vehicles and (iii) combining trajectory information to derive an overall description of traffic flow.…”

Section: Related Workmentioning

confidence: 99%

Classification of traffic video based on a spatiotemporal orientation analysis

Derpanis

Wildes

2011

2011 IEEE Workshop on Applications of Computer Vision (WACV)

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This paper describes a system for classifying traffic congestion videos based on their observed visual dynamics. Central to the proposed system is treating traffic flow identification as an instance of dynamic texture classification. More specifically, a recent discriminative model of dynamic textures is adapted for the special case of traffic flows. This approach avoids the need for segmentation, tracking and motion estimation that typify extant approaches. Classification is based on matching distributions (or histograms) of spacetime orientation structure. Empirical evaluation on a publicly available data set shows high classification performance and robustness to typical environmental conditions (e.g., variable lighting).

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“…Within the car park scene, tracking has been performed using a state of the art generic object tracker [5], set to output the position of object centroids in the image plane. This makes use of adaptive colour models such as those in [8] for both foreground and background, and also incorporates a shape model for vehicle tracking.…”

Section: The Agentsmentioning

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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Tracking multiple vehicles using foreground, background and motion models

Cited by 193 publications

References 7 publications

Detecting Carried Objects in Short Video Sequences

Detecting Carried Objects in Short Video Sequences

Classification of traffic video based on a spatiotemporal orientation analysis

Detecting inexplicable behaviour

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