Understanding andDiagnosing Visual Tracking Systems

Understanding andDiagnosing Visual Tracking Systems

A tracking system usually worksby initializing the observation model with the given bounding box of the targetin the first frame. In each of the following frames, the motion model
firstgenerates candidate regions or proposals for testing based on the estimationfrom the previous frame. The candidate regions or proposals are fed into theobservation model to compute their probability of being the target. The onewith the highest probability
is then selected as the estimation result of thecurrent frame. Based on the output of the observation model, the model updaterdecides whether the observation model needs any update and, if needed, theupdate frequency. Finally, if there are multiple trackers,
the bounding boxesreturned by the trackers will be combined by the ensemble post-processor toobtain a more accurate estimate. This pipeline is illustrated in Fig.

 

Five constituent parts:

1. Motion Model: Based on theestimation from the previous frame, the motion model generates a set ofcandidate regions or bounding boxes which may contain the target
in the currentframe.

2.Feature Extractor: Thefeature extractor represents each candidate in the candidate set using somefeatures.

3.Observation Model: Theobservation model judges whether a candidate is the target based on thefeatures extracted from the candidate.

4.Model Updater: The modelupdater controls the strategy and frequency of updating the observation model. Ithas to strike a balance between model adaptation and drift.

5. Ensemble Post-processor:When a tracking system consists of multiple trackers, the ensemblepostprocessor takes the outputs of the constituent trackers and uses
the ensemblelearning approach to combine them into the final result.

 

Details of all these components

1.Motion Model :ParticleFilter Sliding
,Window Radius ,Sliding Window

2.Feature Extractor: RawGrayscale ,Raw Color , Haar-like Features, HOG ,HOG
+Raw Color

3.Observation Model:LogisticRegression ,Ridge Regression ,SVM ,Structured
Output SVM (SO-SVM)

4.Model Updater:

①update the model whenever the confidence of the target falls below athreshold.

②update the model whenever the difference between the confidence of thetarget and that of the background examples is below a threshold.

5.Ensemble post-processing:

①a loss function for bounding box majority voting and then extended it toincorporate tracker weights, trajectory continuity and removal of bad trackers

②a factorial hidden Markov model that considers the temporal smoothnessbetween frames.

文章来源：http://arxiv.org/abs/1504.06055