Download A Bayesian Computer Vision System for Modeling Human by Oliver N., Rosario B., Pentland A. PDF

By Oliver N., Rosario B., Pentland A.

We describe a real-time machine imaginative and prescient and desktop studying procedure for modeling and spotting human behaviors in a visible surveillance job [1]. The procedure is very involved iviih detecting whilst interactions among humans take place, and classifying the kind of interplay. Examples of fascinating interplay behaviors contain following another individual, changing one's route to meet one other, and so forth.Our process combines top-down with bottom-up info in a closed suggestions loop, with either parts utilizing a statistical Bayesian process. we suggest and examine assorted state-based studying architectures, particularly HMMs and CHMMs. for modeling behaviors and interactions. The CHMM version is proven to paintings even more successfully and accurately.Finally, to house the matter of constrained education info, a man-made 'Alife-style' education approach is used to enhance versatile past types for spotting human interactions. We display the power to exploit those a priori types to correctly classify actual human behaviors and interactions with out extra tuning or education.

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We assume an iterative-based whole-program flow-sensitive pointer analysis that uses a worklist for each procedure, where each worklist maintains a list of unique CFG blocks. An alternative is a single worklist of nodes from a supergraph [16], eliminating procedure boundaries, but we believe that such a large worklist would be too expensive. Our algorithm requires accurate def-use chains. Since definitions are created on the fly during pointer analysis, we need to update chains whenever a new definition is discovered.

5 of the Alpha compiler using optimization level -O3. For all benchmarks we use the test and train input sets for the training runs. For floating-point programs, we use the reference input sets for verification. However, for integer programs, we use the MinneSpec workload [27] in order to save profiling time due to the large memory requirements of the reference input set. We collect the reuse distance distribution by running all programs to completion. 2 Differentiating Multiple Locality Patterns In this section, we experimentally analyze the ability of using branch history to differentiate between multiple locality patterns for a single instruction on our benchmark suite.

4. J. Choi, R. Cytron, and J. Ferrante. Automatic construction of sparse data flow evaluation graphs. In POPL, pages 55–66, 1991. 5. Jamieson M. Cobleigh, Lori A. Clarke, and Leon J. Osterweil. The right algorithm at the right time: comparing data flow analysis algorithms for finite state verification. In Int’l Conf. on Software Engineering, pages 37–46, May 2001. 6. Jeffrey S. Foster, Tachio Terauchi, and Alex Aiken. Flow-sensitive type qualifiers. In ACM SIGPLAN’02 Proc. 2002 PLDI, pages 1–12, June 2002.

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