Dynamic Bayesian Multitarget Tracking for Behavior and Interaction Detection
Lucio Marcenaro, MAURICIO SOTO, Carlo S. Regazzoni · 2013
Contents 18.1 Introduction ................................................................................................................... 489 18.2 Tracking as Bayesian Problem .........................................................................................491 18.3 Interaction Modeling and Learning ............................................................................... 497 18.3.1 Situation Assessment ............................................................................................501 18.3.2 Event Prediction ..................................................................................................501 18.4 Object Tracking with Interaction Analysis ..................................................................... 502 18.4.1 Target Dynamic Model ...................................................................................... 503 18.4.2 Target Observation Model .................................................................................. 503 18.5 Experimental Results...................................................................................................... 504 18.6 Conclusions .................................................................................................................... 509 References ............................................................................................................................... 509 18.1 Introduction Visual tracking represents a fundamental processing step for most of the video analytics for surveillance applications where the aim is to automatically understand the action performed by the objects present in the monitored scene [1-3]. The basic tracking task consists in following a target frame by frame, labeling it, and estimating its trajectory. Although this problem has been widely investigated in the last decades, a solution is still to be found that is valid in general situations without defining tight constraints and assumptions mainly related to the complexity of the guarded scene in terms of number of moving objects and overlapping percentage between objects themselves with environmental obstacles. Crowded scenes in public unconstrained assets such as roads, railway stations, and airports represent a challenging scenario where state-of-the-art tracking algorithms are unable to correctly track each detected target. Therefore, more effective approaches (in terms of target detection and trajectory evaluation precision, object identity preservation, improved robustness to highly cluttered environments) are necessary to correctly perform the tracking task in these scenarios under different environmental conditions (e.g., light changes and nonstatic background). Moreover, research is also focusing on the development of trackers [4] able to enrich available track by including other features such as scale, pose, and shape in the object description with the aim of accomplishing advanced scene interpretation tasks. Furthermore, observation from trackers at different resolution levels can be useful to increase system performances and have a better understanding of the monitored scene.