Automatic human trajectory destination prediction from video

This paper presents an intelligent human trajectory destination detection system from video. The system assumes a passive collection of video from a wide scene used by humans in their daily motion activities such as walking towards a door. The proposed system includes three main modules, namely human blob detection, star skeleton detection and destination area prediction, and it works directly with raw video, producing motion features for destination prediction system, such as position, velocity and acceleration from detected human skeletons, resulting in several input features that are used to train a machine learning classifier. We adopted a university campus exterior scene for the experimental study, which includes 348 pedestrian trajectories from 171 videos and five destination areas: A, B, C, D and E. A total of six data processing combinations and four machine learning classifiers were compared, under a realistic growing window evaluation. Overall, high quality results were achieved by the best model, which uses 37 skeleton motion inputs, undersampling on training data and a random forest. The global discrimination, in terms of area of the receiver operating characteristic curve is around 87%. Furthermore, the best model can predict in advance the five destination classes, obtaining a very good ahead discrimination for classes A, B, C and D, and a reasonable ahead discrimination for class E.