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

1. Multiple Hypothesis Tracking

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. 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. Hypothesis Growth on Typical Scenario
• Tree structures are formed.

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. Track Scoring (cont’d)
• Case 2: No association with the Hypothesis
• Case 3: Hypothesis & Observation Association
• where

7. Track Scoring (cont’d)

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. 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. 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. 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.