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-Two virtual windows are used to determine the path of a single uniformly moving obstacle. If the path of the obstacle crosses the two virtual windows, then its path can be easily determined. A few simulations were implemented to ascertain the viability and effectiveness of this technique. A table with recommended maximum speed of the uniformly moving obstacle was also provided, given the specified pixel resolution and gap between the virtual windows.Index Terms -virtual window, uniformly moving obstacle, path determination.

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The human action classification task is a widely researched topic and is still an open problem. Many state-ofthe-arts approaches involve the usage of bag-of-video-words with spatio-temporal local features to construct characterizations for human actions. In order to improve beyond this standard approach, we investigate the usage of co-occurrences between local features. We propose the usage of co-occurrences information to characterize human actions. A trade-off factor is used to define an optimal trade-off between vocabulary size and classification rate. Next, a spatio-temporal co-occurrence technique is applied to extract co-occurrence information between labeled local features. Novel characterizations for human actions are then constructed. These include a vector quantized correlogram-elements vector, a highly discriminative PCA (Principal Components Analysis) co-occurrence vector and a Haralick texture vector. Multi-channel kernel SVM (support vector machine) is utilized for classification. For evaluation, the well known KTH as well as the UCF-Sports action datasets are used. We obtained state-of-the-arts classification performance. We also demonstrated that we are able to fully utilize co-occurrence information, and improve the standard bag-of-video-words approach.

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