Classifying Tracked Objects and their Interactions from Infrared Imagery

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