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A human trajectory is the likely path a human subject would take to get to a destination. Human trajectory forecasting algorithms try to estimate or predict this path. Such algorithms have wide applications in robotics, computer vision and video surveillance. Understanding the human behavior can provide useful information towards the design of these algorithms. Human trajectory forecasting algorithm is an interesting problem because the outcome is influenced by many factors, of which we believe that the geometry of the environment plays a significant role. In addressing this problem, we have built a model to estimate the occupancy behavior of humans based on the geometry and behavioral norms. We also develop a trajectory forecasting algorithm that understands this occupancy and leverages it for trajectory forecasting in previously unseen geometries. We perform experiments to quantify the error between our prediction model and the trajectories obtained from real world human subjects and compare them to state of the art models. Results obtained suggests a significant enhancement in the accuracy of trajectory forecasting by incorporating the occupancy behavior model.

American film studios collectively produce several hundred movies every year, making the United States the third most prolific producer of films in the world. The budget of these movies is of the order of hundreds of millions of dollars, making their box office success absolutely essential for the survival of the industry. Knowing which movies are likely to succeed and which are likely to fail before the release could benefit the production houses greatly as it will enable them to focus their advertising campaigns which itself cost millions of dollars, accordingly. And it could also help them to know when it is most appropriate to release a movie by looking at the overall market. So the prediction of movie success is of great importance to the industry. Machine learning algorithms are widely used to make predictions such as growth in the stock market, demand for products, nature of tumors etc. This paper presents a detailed study of Logistic Regression, SVM Regression and Linear Regression on IMDB data to predict movie box office.

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