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              In this paper we present the recognition of human actions under view changes. here we Deploy an automotive visual surveillance system to detect abnormal behavior patterns and recognize the normal ones. If a person enters a room, video of him is captured and stored(both side view and the top view) then it is given to the training module here the video is checked if it is a normal behavior splited image is taken, whenever the action is recognized blob images are saved, and the frame counts are taken . In case, the anomaly is detected the red color will be displayed. The abnormal behavior is achieved by keep tracking the videos and blob frames and checking each frame values.

System Requirement Specification:-
DOMAIN :Transactions on Pattern Analysis and Machine intelligence,
SOFTWARE : Operating System: windows xp, Platform: JAVA, Database : MySQL
Special Tool : Java Media Framework, Protocol : TCP
HARDWARE : Processor: Pentium-IV, Speed: 1.8 GHZ, RAM: 512 MB, HDD: 80 GB

              We propose a novel unsupervised learning framework to model activities and interactions in crowded and complicated scenes. Under our framework, hierarchical Bayesian models are used to connect three elements in visual surveillance: low-level visual features, simple �atomic� activities, and interactions. Atomic activities are modeled as distributions over low-level visual features; andmultiagent interactions are modeled as distributions over atomic activities. These models are learned in an unsupervised way. Given along video sequence, moving pixels are clustered into different atomic activities and short video clips are clustered into different interactions. In this paper, we propose three hierarchical Bayesian models: the Latent Dirichlet Allocation (LDA) mixture model, the Hierarchical Dirichlet Processes (HDP) mixture model, and the Dual Hierarchical Dirichlet Processes (Dual-HDP) model. They advance existing topic models, such as LDA [1] and HDP [2]. Directly using existing LDA and HDP models under our framework, only moving pixels can be blustered into atomic activities. Our models can cluster both moving pixels and video clips into atomic activities and into interactions. The LDA mixture model assumes that it is already known how many different types of atomic activities and interactions occur in the scene. The HDP mixture model automatically decides the number of categories of atomic activities. The Dual-HDP automatically decides the numbers of categories of both atomic activities and interactions. Our data sets are challenging video sequences from crowded traffic scenes and train station scenes with many kinds of activities co-occurring. Without tracking and human labeling effort, our framework completes many challenging visual surveillance tasks of broad interest such as: 1) discovering and providing a summary of typical atomic activities and interactions occurring in the scene, 2) segmenting long video sequences into different interactions, 3) segmenting motions into different activities, 4) detecting abnormality, and 5) supporting high-level queries on activities and interactions. In our work, these surveillance problems are formulated in a transparent, clean, and probabilistic way compared with the ad hoc nature of many existing approaches.

System Requirement Specification:-
DOMAIN     : TRANSACTIONS ON PATTERN ANALYSYS AND MACHINE INTELLIGENCE
SOFTWARE : Operating System: windows xp Platform: JAVA/J2EE TOOL: JMF
HARDWARE : Processor:Pentium-IV Speed: 1.8 GHZ,RAM: 512 MB,HDD: 80 GB

                  The resource sharing nature of Simultaneous Multithreading (SMT) processors and the presence of long latency instructions from concurrent threads make the instruction scheduling window (IW), which is a primary shared component among key pipeline structures in SMT, a performance bottleneck. Due to the tight constraints on its physical size, the IW faces more severe pressure to handle the instructions from various threads while attempting to avoid resource monopolization by some low-ILP threads. It is particularly challenging to optimize the efficiency and fairness in IW utilization to fulfill the affordable performance by SMT under the shadow of long latency instructions. Most of the existing optimization schemes in SMT processors rely on the fetch policy to control the instructions that are allowed to enter the pipeline, while little effort is put to control the long latency instructions that are already located in the IW. In this paper, we propose streamline buffers to handle the long latency instructions that have already entered the pipeline and clog the IW, while the controlling fetch policies take time to react. Each streamline buffer extracts from IW and holds a chain of instructions from a thread that are stalled by dependency on a long latency load. When the load value returns, the streamline buffer then serves these instructions directly to in-order execution, avoiding any instruction replay. This is done in supplement to the conventional IW that serves in parallel the other instructions for out-of-order (o-o-o) execution. Analysis of SPEC2000 integer and FP benchmarks reveals that instructions dependent on long latency loads, typically have their first source operand ready within 5 percent-15 percent of their total wait time in the IW. Our scheme is able to utilize this asymmetry in source operands' ready time to achieve a complexity effective design. As compared to the baseline SMT architecture, our design when working in conjunction with earlier propose- - d ICOUNT.2.8 fetch policy for 4-threads effectively reduces the IW full rate by 9.4 percent (11 percent for 2-thread), improves average IPC for MIXED workloads by 9.6 percent (8 percent for MEM workloads and 4.4 percent for CPU workloads), and fairness by 7.56 percent (7.24 percent for 2-thread). Similar enhancements are observed when run in conjunction with an RR.2.8 fetch policy.

System Requirement Specification:-
DOMAIN     : TRANSACTIONS ON PATTERN ANALYSYS AND MACHINE INTELLIGENCE
SOFTWARE : Operating System: windowsxp Platform: JAVA
HARDWARE : Processor: Pentium-IV Speed: 1.8 GHZ,RAM:512 MB,HDD: 80 GB

                 This paper aims to address of modeling video behavior captured in surveillance video for the applications of online normal behavior recognition and anomaly detection. A novel framework is developed for automatic behavior profiling and online anomaly sampling detection with out any manual labeling of the training data set. The frame work consists of the following key components: 1) A compact and effective behavior representation method is developed based on discrete scene event detection. The similarity between behavior patterns is measured based on modeling each pattern using Dynamic Bayesian Network (DBN). 2) The natural grouping of behavior patterns is discovered through novel spectral clustering algorithm with unsupervised model selection and future selection on the eigenvectors of a normalized affinity matrix. 3) A composite generative behavior model is constructed that is capable of generalizing from a small training set to accommodate variations in unseen normal behavio patterns.4) A runtime accumulative anomaly measure is introduced to detect abnormal behavior, where as normal behavior patterns are recognized when sufficient visual evidence has become available based on online Likelihood Ratio Test(LRT) method. This ensures robust and reliable anomaly detection and normal behavior recognition at the shortest possible time. The effectiveness and robustness of our approach is demonstrated through experiments using noisy and sparse data sets collected from both indoor and outdoor surveillances scenarios. In particular, it is shown that a behavior model trained using unlabeled data set is superior to those trained using same but labeled data set in detecting anomaly from an unseen video. The experiments also suggest that our online LRT-based behavior recognition is advantageous over the commonly used maximum Likelihood (ML) method in differentiating ambiguities among different behavior classes observed on line.

System Requirement Specification:-
DOMAIN     : TRANSACTIONS ON PATTERN ANALYSYS AND MACHINE INTELLIGENCE
SOFTWARE : Operating System: windows xp, Platform: JAVA, DB: MySQL
HARDWARE : Processor: Pentium-IV Speed:1.8 GHZ,RAM: 512 MB,HDD: 80 GB