Computational complexity analysis for multiple hypothesis tracking
نویسندگان
چکیده
منابع مشابه
Computational Complexity of Hypothesis Assembly
T h e p rob lem of f inding a best exp lana t ion of a set of da ta has been a top ic of m u c h interest in A r t i f i c i a l I n t e l l i gence. In th is paper we present an approach to th is p r o b l e m by hypothesis assembly. We present th is approach f o rma l l y so tha t we can examine the t ime comp lex i t y and correctness of the a lgo r i t hms . We then examine a system i m p l...
متن کاملBayesian network for multiple hypothesis tracking
For a flexible camera-to-camera tracking of multiple objects we model the object’s behavior with a Bayesian network and combine it with the multiple hypothesis framework that associates observations with objects. Bayesian networks offer a possibility to factor complex, joint distributions into a product of intuitive conditional densities describing and predicting the object’s path. Yet, these m...
متن کاملCost-function-based hypothesis control techniques for multiple hypothesis tracking
The problem of tracking targets in clutter naturally leads to a Gaussian mixture representation of the probability density function of the target state vector. Modern tracking methods maintain the mean, covariance and probability weight corresponding to each hypothesis, yet they rely on simple merging and pruning rules to control the growth of hypotheses. This paper proposes a structured, cost-...
متن کاملTopics in Multiple-Hypothesis Tracking
This manuscript discusses some recent advances in multi-target tracking. First, we describe a target kinematic motion model that has a number of appealing characteristics and that, to our knowledge, is not in use within the data fusion community. We describe recent advances in multiple-hypothesis tracking, both in the traditional setting where measurements are informative with respect to target...
متن کاملAdaptive State Multiple-Hypothesis Tracking
In tracking algorithms where measurements from various sensors are combined the track state representation is usually dependent on the type of sensor information that is received. When a multi-hypothesis tracking algorithm is used the probabilities of the different hypotheses containing tracks in different representations need to be re-evaluated when track state representations are changed. For...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Mathematical and Computer Modelling
سال: 1999
ISSN: 0895-7177
DOI: 10.1016/s0895-7177(99)00077-1