SwePub
Sök i LIBRIS databas

  Utökad sökning

id:"swepub:oai:lup.lub.lu.se:290c25b3-674f-4a4e-819e-1ac3c34e79cb"
 

Sökning: id:"swepub:oai:lup.lub.lu.se:290c25b3-674f-4a4e-819e-1ac3c34e79cb" > Multi-target Tracki...

Multi-target Tracking Using on-line Viterbi Optimisation and Stochastic Modelling

Ardö, Håkan (författare)
Lund University,Lunds universitet,Matematik LTH,Matematikcentrum,Institutioner vid LTH,Lunds Tekniska Högskola,Mathematics (Faculty of Engineering),Centre for Mathematical Sciences,Departments at LTH,Faculty of Engineering, LTH
 (creator_code:org_t)
2009
Engelska 171 s.
  • Doktorsavhandling (övrigt vetenskapligt/konstnärligt)
Abstract Ämnesord
Stäng  
  • To study and compare the safety of intersection, traffic scientists today typically manually monitor the intersection during several days and count how often certain events such as evasive manoeuvres occur. This is a laboursome and costly procedure. The aim of this thesis is to provide tools that can reduce the amount of manual labour required by using automated video analytics. Two methods for creating for such tools are presented. The first method is a probabilistic background foreground segmentation that for each block of pixels calculate the probability that this block currently views the static background or some moving foreground object. This is done by deriving the probability distribution of the normalised cross correlation in the background and the foreground case respectively. The background distribution depends on the amount of structure in the block. The second method is a multi-target tracker that uses the probabilistic background foreground segmentation to produce the trajectories of all objects in the scene. It operates online but with a few seconds delay in order to incorporate information from both past and future frames when deciding on the current state. This means that the output is guaranteed to be consistent, i.e. no jumping between different hypothesis, and the respect constrains placed on the system such as "objects may not occupy the same space at the same time" or "objects may only appear at the border of the image". The methods have been tested both on synthetic and numerous sets of real data by implementing applications such as people counting, loitering detection and traffic surveillance. The applications have been shown to perform very well as long as the scene studied is not too large.

Ämnesord

NATURVETENSKAP  -- Data- och informationsvetenskap -- Datorseende och robotik (hsv//swe)
NATURAL SCIENCES  -- Computer and Information Sciences -- Computer Vision and Robotics (hsv//eng)
NATURVETENSKAP  -- Matematik (hsv//swe)
NATURAL SCIENCES  -- Mathematics (hsv//eng)

Publikations- och innehållstyp

dok (ämneskategori)
vet (ämneskategori)

Hitta via bibliotek

Till lärosätets databas

Hitta mer i SwePub

Av författaren/redakt...
Ardö, Håkan
Om ämnet
NATURVETENSKAP
NATURVETENSKAP
och Data och informa ...
och Datorseende och ...
NATURVETENSKAP
NATURVETENSKAP
och Matematik
Av lärosätet
Lunds universitet

Sök utanför SwePub

Kungliga biblioteket hanterar dina personuppgifter i enlighet med EU:s dataskyddsförordning (2018), GDPR. Läs mer om hur det funkar här.
Så här hanterar KB dina uppgifter vid användning av denna tjänst.

 
pil uppåt Stäng

Kopiera och spara länken för att återkomma till aktuell vy