Interacting Object Tracking in Crowded Urban Areas

Abstract

Tracking in crowded urban areas is a daunting task. High crowdedness causes challenging data association problems. Different motion patterns from a wide variety of moving objects make motion modeling difficult. Accompanying with traditional motion modeling techniques, this paper introduces a scene interaction model and a neighboring object interaction model to respectively take long-term and short-term interactions between the tracked objects and its surroundings into account. With the use of the interaction models, anomalous activity recognition is accomplished easily. In addition, move-stop hypothesis tracking is applied to deal with move-stop-move maneuvers. All these approaches are seamlessly inter-graded under the variable-structure multiple-model estimation framework. The proposed approaches have been demonstrated using data from a laser scanner mounted on the PALI robot at a crowded intersection. Interacting pedestrians, bicycles, motorcycles, cars and trucks are successfully tracked in difficult situations with occlusion.