Robust multi-Bernoulli filtering for visual tracking
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To achieve reliable multi-object filtering in vision application, it is of great importance to determine appropriate model parameters. Parameters such as motion and measurement noise covariance can be chosen based on the image frame rate and the property of the designed detector. However, it is not trivial to obtain the average number of false positive measurements or detection probability due to the arbitrary visual scene characteristics from illumination condition or different fields of view. In this paper, we introduce the recently proposed robust multi-Bernoulli filter to deal with unknown clutter rate and detection profile in visual tracking applications. The robust multi-Bernoulli filter treats false positive responses as a special type of target so that the unknown clutter rate is estimated based on the estimated number of clutter targets. Performance evaluation with real videos demonstrates the effectiveness of the robust multi-Bernoulli filter and comparison results with the standard multi-object tracking algorithm show its reliability.
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Reuter, S.; Vo, Ba Tuong; Vo, Ba-Ngu; Dietmayer, K. (2014)In this paper, we propose the labeled multi-Bernoulli filter which explicitly estimates target tracks and provides a more accurate approximation of the multi-object Bayes update than the multi-Bernoulli filter. In particular, ...
Jones, B.; Vo, Ba Tuong; Vo, Ba-Ngu (2016)Space-object tracking systems require robust and accurate methods of multi-target state estimation and prediction. This paper presents the application of labeled multi-Bernoulli filters for space-object tracking, and ...
Reuter, S.; Vo, Ba Tuong; Vo, Ba-Ngu; Dietmayer, K. (2014)This paper proposes a generalization of the multi- Bernoulli filter called the labeled multi-Bernoulli filter that outputs target tracks. Moreover, the labeled multi-Bernoulli filter does not exhibit a cardinality bias ...