{"id":10,"date":"2022-04-28T18:24:20","date_gmt":"2022-04-28T18:24:20","guid":{"rendered":"https:\/\/mscvprojects.ri.cmu.edu\/2022team11\/?page_id=10"},"modified":"2022-12-21T01:08:21","modified_gmt":"2022-12-21T01:08:21","slug":"introduction","status":"publish","type":"page","link":"https:\/\/mscvprojects.ri.cmu.edu\/2022team11\/","title":{"rendered":"Project Summary"},"content":{"rendered":"\n

Motivation<\/h1>\n\n\n\n

Modeling and understanding pedestrian behavior is an important component of building safe and secure smart cities. It is one of the primary components of video surveillance and has drawn increasing attention in recent years for various applications like pedestrian walking path prediction, traffic flow segmentation, crowd counting and segmentation, and abnormal event detection. So why predict pedestrian trajectories? Some applications are: <\/p>\n\n\n\n

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Human Behavior Analysis:<\/strong>
1. Security Surveillance
2. Action Prediction
3. Planning of intersections<\/figcaption><\/figure>\n\n\n\n
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Safety in Autonomous vehicles:<\/strong>
1. Trajectory planning
2. Improves safety<\/figcaption><\/figure>\n\n\n\n

Why is it challenging?<\/h1>\n\n\n\n

Pedestrian behavior modeling is challenging, especially for scenes with crowds. Previous studies have shown that the walking behavior of an individual can be influenced by a variety of factors including scene layout (e.g. entrances, exits, walls, and obstacles), pedestrian beliefs (the choice of source and destination), and interactions with other moving pedestrians. <\/p>\n\n\n\n

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Understanding the context of human behavior and actions is challenging<\/figcaption><\/figure>\n\n\n\n

As seen in the figure above, the same action can have different meanings based on the context and situation. Thus, understanding the context of specific human actions can help predict anomalous activities like crimes in advance. This will ultimately enable us to build behavioral twins of intersections in smart cities. <\/p>\n\n\n\n

Problem Statement<\/h1>\n\n\n\n