Multiple Hypothesis Tracking
Principle
• The hypotheses are propagated into the future in
anticipation that subsequent data will resolve the
uncertaint...
Hypothesis Formation
• The track hypotheses that are at leaves are
«Active Hypotheses».
• New observations are associated ...
Hypothesis Growth on Typical Scenario
• Tree structures are formed.
Track Scoring
• The evaluation of alternative track formation
hypotheses requires a probabilistic expression
that includes...
Track Scoring (cont’d)
• Case 2: No association with the Hypothesis
• Case 3: Hypothesis & Observation Association
• where
Track Scoring (cont’d)
Practical Issues
• There is clearly a potential combination
explosion in the number of hypotheses that an
MHT system can g...
Practical Issues – I Clustering
• The operation of clustering is performed to
reduce the number of hypotheses that must be...
Practical Issues –II Hypothesis Pruning
• Trees should always be pruned not to exceed
N scan.
• Track Hypotheses that are ...
Practical Issues –III Track Hypothesis
Merging & Seperation
• Compatibility: Tracks that do not share any
common observati...
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Multiple Hypothesis Tracking Algorithm

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It is a tracking algorithm that decides with last N scans. This makes tracks more reliable. General algorithm and track scores are explained.

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Multiple Hypothesis Tracking Algorithm

  1. 1. Multiple Hypothesis Tracking
  2. 2. Principle • The hypotheses are propagated into the future in anticipation that subsequent data will resolve the uncertainty. • The key principle of the MHT method is that difficult data association decisions are deferred until more data are received. • An observation may be a – False alarm ( Continued Several Times) – Associated with a Track – An observation that initiates Track
  3. 3. Hypothesis Formation • The track hypotheses that are at leaves are «Active Hypotheses». • New observations are associated to these or not and new Hypothesis can be formed. • The depth of tree is held at «N».
  4. 4. Hypothesis Growth on Typical Scenario • Tree structures are formed.
  5. 5. Track Scoring • The evaluation of alternative track formation hypotheses requires a probabilistic expression that includes all aspects of the data association problem. • Case 1: Track Initiation
  6. 6. Track Scoring (cont’d) • Case 2: No association with the Hypothesis • Case 3: Hypothesis & Observation Association • where
  7. 7. Track Scoring (cont’d)
  8. 8. Practical Issues • There is clearly a potential combination explosion in the number of hypotheses that an MHT system can generate. • Measures to Keep Hypothesis Number Feasible – Clustering – Hypothesis Pruning – Track Merging & Seperation
  9. 9. Practical Issues – I Clustering • The operation of clustering is performed to reduce the number of hypotheses that must be generated and evaluated. • Clusters are collections of tracks that are linked by common observations. (Not necessarily direct) • Within each cluster, hypotheses are evaluated and low probability hypotheses and tracks are deleted. ( 0.8 * Max) & ( Top 5 Hypothesis)
  10. 10. Practical Issues –II Hypothesis Pruning • Trees should always be pruned not to exceed N scan. • Track Hypotheses that are below certain value must be pruned. • Track Hypotheses that are not continued for M scan must be pruned. • Track Hypotheses that loses all children to pruning must also be pruned.
  11. 11. Practical Issues –III Track Hypothesis Merging & Seperation • Compatibility: Tracks that do not share any common observations will be defined to be compatible. • They can represent at most one target. • Each scan compatibility check must be done and merging and seperation.
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