In multi-target tracking,Multiple Hypothesis Tracking (MHT) can effectively solve the data association problem. However,traditional MHT can not make full use of motion information. In this work,we combine MHT with Int...In multi-target tracking,Multiple Hypothesis Tracking (MHT) can effectively solve the data association problem. However,traditional MHT can not make full use of motion information. In this work,we combine MHT with Interactive Multiple Model (IMM) estimator and feature fusion. New algorithm greatly improves the tracking performance due to the fact that IMM estimator provides better estimation and feature information enhances the accuracy of data association. The new algorithm is tested by tracking tropical fish in fish container. Experimental result shows that this algorithm can significantly reduce tracking lost rate and restrain the noises with higher computational effectiveness when compares with traditional MHT.展开更多
Although it is a relatively new field of study, the animal cognition literature is quite extensive and difficult to synthesize. This paper explores the contributions a comprehensive, computational, cognitive model can...Although it is a relatively new field of study, the animal cognition literature is quite extensive and difficult to synthesize. This paper explores the contributions a comprehensive, computational, cognitive model can make toward organizing and assimilating this literature, as well as toward identifying important concepts and their interrelations. Using the LIDA model as an example, a framework is described within which to integrate the diverse research in animal cognition. Such a framework can provide both an ontology of concepts and their relations, and a working model of an animal＇s cognitive processes that can compliment active empirical research. In addition to helping to account for a broad range of cognitive processes, such a model can help to comparatively assess the cognitive capabilities of different animal species. After deriving an ontology for animal cognition from the LIDA model, we apply it to develop the beginnings of a database that maps the cognitive facilities of a variety of animal species. We conclude by discussing future avenues of research, particularly the use of computational models of animal cognition as valuable tools for hypotheses generation and testing [Current Zoology 57 （4）： 499-513, 2011].展开更多
We introduce the so-called naive tests and give a brief review of the new developments. Naive testing methods are easy to understand and perform robustly, especially when the dimension is large. We focus mainly on rev...We introduce the so-called naive tests and give a brief review of the new developments. Naive testing methods are easy to understand and perform robustly, especially when the dimension is large. We focus mainly on reviewing some naive testing methods for the mean vectors and covariance matrices of high-dimensional populations, and we believe that this naive testing approach can be used widely in many other testing problems.展开更多
基金Supported by the National Natural Science Foundation of China (No. 60772154)the President Foundation of Graduate University of Chinese Academy of Sciences (No. 085102GN00)
文摘In multi-target tracking,Multiple Hypothesis Tracking (MHT) can effectively solve the data association problem. However,traditional MHT can not make full use of motion information. In this work,we combine MHT with Interactive Multiple Model (IMM) estimator and feature fusion. New algorithm greatly improves the tracking performance due to the fact that IMM estimator provides better estimation and feature information enhances the accuracy of data association. The new algorithm is tested by tracking tropical fish in fish container. Experimental result shows that this algorithm can significantly reduce tracking lost rate and restrain the noises with higher computational effectiveness when compares with traditional MHT.
基金Sidney D＇Mello was supported by the National Science Foundation （ITR 0325428, HCC 0834847） and the Institute of Education Sciences （R305A080594）. Any opinions, findings and conclusions, or recommendations expressed in this paper are those of the authors and do not necessarily reflect the views of NSF and IES.
文摘Although it is a relatively new field of study, the animal cognition literature is quite extensive and difficult to synthesize. This paper explores the contributions a comprehensive, computational, cognitive model can make toward organizing and assimilating this literature, as well as toward identifying important concepts and their interrelations. Using the LIDA model as an example, a framework is described within which to integrate the diverse research in animal cognition. Such a framework can provide both an ontology of concepts and their relations, and a working model of an animal＇s cognitive processes that can compliment active empirical research. In addition to helping to account for a broad range of cognitive processes, such a model can help to comparatively assess the cognitive capabilities of different animal species. After deriving an ontology for animal cognition from the LIDA model, we apply it to develop the beginnings of a database that maps the cognitive facilities of a variety of animal species. We conclude by discussing future avenues of research, particularly the use of computational models of animal cognition as valuable tools for hypotheses generation and testing [Current Zoology 57 （4）： 499-513, 2011].
基金supported by National Natural Science Foundation of China (Grant Nos. 11301063 and 11571067)Science and Technology Development Foundation of Jilin (Grant No. 20160520174JH)Science and Technology Foundation of Jilin during the "13th Five-Year Plan"
文摘We introduce the so-called naive tests and give a brief review of the new developments. Naive testing methods are easy to understand and perform robustly, especially when the dimension is large. We focus mainly on reviewing some naive testing methods for the mean vectors and covariance matrices of high-dimensional populations, and we believe that this naive testing approach can be used widely in many other testing problems.
