Classifying Tracked Objects and their Interactions from Infrared Imagery
El Amar, Xavier Maldague · 2006
For many intelligent security systems the use of infrared technology is becoming essential and is a challenging issue. This paper outlines a framework for exploiting spatio-temporal tracking parameters to classify multiple moving objects and recognize their interactions using low quality thermal imagery. For outdoor scenes, motion segmentation is automatically performed using a novel dynamic background-subtraction technique which robustly adapts detection to illumination changes. During the tracking process, the algorithm uses thermal data and motion parameters to label moving objects into two main classes: persons and vehicles. However, the calibrated temperature informations are employed to locate the "part of interest" of each classified object: head's parts for persons and engine's part for vehicles. Once these tasks are correctly performed, the algorithm applies predefined rules to recognize some interactions between classified objects. The reasoning rules are based on the class's labels and motion parameters to partition interactions according to their authors: events with a single author and events with multiple authors. For each of them, several actions of interest can be identified by testing specific criterion's combinations. This partition is the base of our algorithmic reasoning and increases considerably the process's speed. Thus, with this complete architecture, the system is able to automatically obtain relevant video surveillance scenarios useful as powerful tools for the decision-making aid and also for archiving. Finally, experiments proved that the algorithm is efficient and fast enough to operate in real-time implementation