Multi-target tracking in clutter without measurement assignment

Mušicki, D.; Scala, B.F. La; Evans, Robin J.

doi:10.1109/cdc.2004.1428733

Cited by 54 publications

(91 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In our framework, each mixture component in (7), p k (x t−1 |z 1:t−1 ), represents an independent dynamic system 2 that has a separate measurement model. Note that the combined posterior induced by the weighted sum is also a reasonable alternative to estimate target state with multiple sensors as in Blom and Bar-Shalom (1988), Hoffmann andDang (2009), Jia et al (2008), Mazor et al (1998), Musicki (2008), Musicki and La Scala (2008) although it is not a standard method to combine the measurements from multiple sensors by Bayesian way-product of likelihood densities.…”

Section: Fusion Tracking By Mixture Kbfmentioning

confidence: 97%

“…Mixture models for density propagation in the sequential Bayesian filtering framework have been previously used to handle multi-modality. Interacting Multiple Model (IMM) filters are employed to model multiple dynamics (Blom and Bar-Shalom 1988;Jia et al 2008;Mazor et al 1998;Musicki 2008;Musicki and La Scala 2008) or multiple measurements (Hoffmann and Dang 2009) effectively. Also, the mixture particle filter shows superior performance in preserving and tracking multiple modes in the posterior density function (Vermaak et al 2003).…”

Section: Fusion Tracking By Mixture Kbfmentioning

confidence: 99%

See 1 more Smart Citation

Multi-Camera Tracking with Adaptive Resource Allocation

2010

View full text Add to dashboard Cite

Sensor fusion for object tracking is attractive since the integration of multiple sensors and/or algorithms with different characteristics can improve performance. However, there exist several critical limitations to sensor fusion techniques: (1) the measurement cost increases typically as many times as the number of sensors, (2) it is not straightforward to measure the confidence of each source and give it a proper weight for state estimation, and (3) there is no principled dynamic resource allocation algorithm for better performance and efficiency. We describe a method to fuse information from multiple sensors and estimate the current tracker state by using a mixture of sequential Bayesian filters (e.g., particle filter)-one filter for each sensor, where each filter makes a different level of contribution to estimate the combined posterior in a reliable manner. In this framework, multiple sensors interact to determine an appropriate sensor for each particle dynamically; each particle is allocated to only one of the sensors for measurement and a different number of particles is assigned to each sensor. The level of the contribution of each sensor changes dynamically based on its prior information and relative measurement confidence. We apply this technique to visual track-B. Han ( ) ing with multiple cameras, and demonstrate its effectiveness through tracking results in videos.

show abstract

Section: Fusion Tracking By Mixture Kbfmentioning

confidence: 97%

Section: Fusion Tracking By Mixture Kbfmentioning

confidence: 99%

Multi-Camera Tracking with Adaptive Resource Allocation

2010

View full text Add to dashboard Cite

show abstract

“…Several approximation approaches have been proposed such as the deterministic strategies in (83-89) and the MCMC-based strategies in Reference 90. Moreover, since the basic JPDAF can only accommodate a fixed and known number of targets, several novel extensions have been proposed to accommodate an unknown and time-varying number of targets, such as the joint integrated PDAF (JIPDAF) (91) along with an efficient implementation (92), and automatic track formation (ATF) (93). Further details on the JPDAF are given in Section 4.…”

Section: The Mtt Problemmentioning

confidence: 99%

Multitarget Tracking

Mallick²,

Bar‐Shalom

et al. 2015

Wiley Encyclopedia of Electrical and Electronics Engineering

212

150

View full text Add to dashboard Cite

Multitarget tracking (MTT) refers to the problem of jointly estimating the number of targets and their states or trajectories from noisy sensor measurements. MTT has a long history spanning over 50 years, with a plethora of applications in many fields of study. While numerous techniques have been developed, the three most widely used approaches to MTT are the joint probabilistic data association filter (JPDAF), multiple hypothesis tracking (MHT), and random finite set (RFS). The JPDAF and MHT have been widely used for more than two decades, while the RFS‐based MTT algorithms have received a great deal of attention during the last decade. In this article, we provide an overview of MTT and succinct summaries of popular state‐of‐the‐art MTT algorithms.

show abstract

“…Most approaches include tracking, usually in various flavors of Kalman or particle filtering [13], [14], [15], [16], [17], [18], [19]. Both tracking methods perform a kind of "late fusion", combining detection results with a motion model, the latter having no infleucne at all on detection process.…”

Section: A Related Workmentioning

confidence: 99%

Robust visual pedestrian detection by tight coupling to tracking

Gepperth

Sattarov

Heisele

et al. 2014

17th International IEEE Conference on Intelligent Transportation Systems (ITSC)

View full text Add to dashboard Cite

Abstract-In this article, we propose a visual pedestrian detection system which couples pedestrian appearance and pedestrian motion in a Bayesian fashion, with the goal of making detection more invariant to appearance changes. More precisely, the system couples dense appearance-based pedestrian likelihoods derived from a sliding-window SVM detector to spatial prior distributions obtained from the prediction step of a particle filter based pedestrian tracker. This mechanism, which we term dynamic attention priors (DAP), is inspired by recent results on predictive visual attention in humans and can be implemented at negligible computational cost. We prove experimentally, using a set of public, annotated pedestrian sequences, that detection performance is improved significantly, especially in cases where pedestrians differ from the learned models, e.g., when they are too small, have an unusual pose or occur before strongly structured backgrounds. In particular, dynamic attention priors allow to use more restrictive detection thresholds without losing detections while minimizing false detections.

show abstract

Multi-target tracking in clutter without measurement assignment

Cited by 54 publications

References 24 publications

Multi-Camera Tracking with Adaptive Resource Allocation

Multi-Camera Tracking with Adaptive Resource Allocation

Multitarget Tracking

Robust visual pedestrian detection by tight coupling to tracking

Contact Info

Product

Resources

About