In this paper we address the problem of recognizing, localizing and tracking multiple objects of different categories in meeting room videos. Difficulties such as lack of detail and multi-object co-occurrence make it hard to directly apply traditional object recognition methods. Under such circumstances, we show that incorporating object-level spatio-temporal relationships can lead to significant improvements in inference of object category and state. Contextual relationships are modeled by a dynamic Markov random field, in which recognition, localization and tracking are done simultaneously. Further, we define human as the key object of the scene, which can be detected relatively robustly and therefore is used to guide the inference of other objects. Experiments are done on the CHIL meeting video corpus. Performance is evaluated in terms of object detection and false alarm rates, object recognition confusion matrix and pixel-level accuracy of object segmentation.
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