6,508 research outputs found
Enhanced tracking and recognition of moving objects by reasoning about spatio-temporal continuity.
A framework for the logical and statistical analysis and annotation of dynamic scenes containing occlusion and other uncertainties is presented. This framework consists
of three elements; an object tracker module, an object recognition/classification module and a logical consistency, ambiguity and error reasoning engine. The principle behind the object tracker and object recognition modules is to reduce error by increasing ambiguity (by merging objects in close proximity and presenting multiple
hypotheses). The reasoning engine deals with error, ambiguity and occlusion in a unified framework to produce a hypothesis that satisfies fundamental constraints
on the spatio-temporal continuity of objects. Our algorithm finds a globally consistent model of an extended video sequence that is maximally supported by a voting function based on the output of a statistical classifier. The system results
in an annotation that is significantly more accurate than what would be obtained
by frame-by-frame evaluation of the classifier output. The framework has been implemented
and applied successfully to the analysis of team sports with a single
camera.
Key words: Visua
Poisson multi-Bernoulli mixture trackers: continuity through random finite sets of trajectories
The Poisson multi-Bernoulli mixture (PMBM) is an unlabelled multi-target
distribution for which the prediction and update are closed. It has a Poisson
birth process, and new Bernoulli components are generated on each new
measurement as a part of the Bayesian measurement update. The PMBM filter is
similar to the multiple hypothesis tracker (MHT), but seemingly does not
provide explicit continuity between time steps. This paper considers a recently
developed formulation of the multi-target tracking problem as a random finite
set (RFS) of trajectories, and derives two trajectory RFS filters, called PMBM
trackers. The PMBM trackers efficiently estimate the set of trajectories, and
share hypothesis structure with the PMBM filter. By showing that the prediction
and update in the PMBM filter can be viewed as an efficient method for
calculating the time marginals of the RFS of trajectories, continuity in the
same sense as MHT is established for the PMBM filter
The Greedy Dirichlet Process Filter - An Online Clustering Multi-Target Tracker
Reliable collision avoidance is one of the main requirements for autonomous
driving. Hence, it is important to correctly estimate the states of an unknown
number of static and dynamic objects in real-time. Here, data association is a
major challenge for every multi-target tracker. We propose a novel multi-target
tracker called Greedy Dirichlet Process Filter (GDPF) based on the
non-parametric Bayesian model called Dirichlet Processes and the fast posterior
computation algorithm Sequential Updating and Greedy Search (SUGS). By adding a
temporal dependence we get a real-time capable tracking framework without the
need of a previous clustering or data association step. Real-world tests show
that GDPF outperforms other multi-target tracker in terms of accuracy and
stability
An efficient message passing algorithm for multi-target tracking
We propose a new approach for multi-sensor multi-target tracking by constructing statistical models on graphs with continuous-valued nodes for target states and discrete-valued nodes for data association hypotheses. These graphical representations lead to message-passing algorithms for the fusion of data across time, sensor, and target that are radically different than algorithms such as those found in state-of-the-art multiple hypothesis tracking (MHT) algorithms. Important differences include: (a) our message-passing algorithms explicitly compute different probabilities and estimates than MHT algorithms; (b) our algorithms propagate information from future data about past hypotheses via messages backward in time (rather than doing this via extending track hypothesis trees forward in time); and (c) the combinatorial complexity of the problem is manifested in a different way, one in which particle-like, approximated, messages are propagated forward and backward in time (rather than hypotheses being enumerated and truncated over time). A side benefit of this structure is that it automatically provides smoothed target trajectories using future data. A major advantage is the potential for low-order polynomial (and linear in some cases) dependency on the length of the tracking interval N, in contrast with the exponential complexity in N for so-called N-scan algorithms. We provide experimental results that support this potential. As a result, we can afford to use longer tracking intervals, allowing us to incorporate out-of-sequence data seamlessly and to conduct track-stitching when future data provide evidence that disambiguates tracks well into the past
Predicting Multiple Target Tracking Performance for Applications on Video Sequences
This dissertation presents a framework to predict the performance of multiple target tracking (MTT) techniques. The framework is based on the mathematical descriptors of point processes, the probability generating functional (p.g.fl). It is shown that conceptually the p.g.fls of MTT techniques can be interpreted as a transform that can be marginalized to an expression that encodes all the information regarding the likelihood model as well as the underlying assumptions present in a given tracking technique. In order to use this approach for tracker performance prediction in video sequences, a framework that combines video quality assessment concepts and the marginalized transform is introduced. The multiple hypothesis tracker (MHT), Joint Probabilistic Data Association (JPDA), Markov Chain Monte Carlo (MCMC) data association, and the Probability Hypothesis Density filter (PHD) are used as a test cases. We introduce their transforms and perform a numerical comparison to predict their performance under identical conditions. We also introduce the concepts that present the base for estimation in general and for applications in computer vision
- …