Pedestrian recognition in multi-camera networks using multilevel important salient feature and multicategory incremental learning

The ability to recognize pedestrians across multiple camera views is of great importance for many applications in the broad field of video surveillance. Due to the absence of the topology and calibration of distributed cameras, spatio-temporal reasoning becomes unavailable, and therefore only appearance information can be used in real-world scenarios, especially for disjoint camera views. This paper proposes a novel approach based on important salient feature and multi-category transfer incremental learning to recognize pedestrians for long-term tracking in multi-camera networks without space-time cues. An accurate and robust model can be built for pedestrian recognition using few samples. We first propose a novel multi-level important salient feature detection method (MImSF1) to formulate the appearance model. Due to environmental changes, the appearances of the pedestrians under the camera can change over time and across space, therefore the classification performance may be impaired. Hence, the appearance models should be continuously updated. We then adopt a novel object recognition multicategory incremental modeling algorithm (ORMIM2) to update the appearance model adaptively and recognize the pedestrians based on a classification approach. One of the major advantages of the proposed method is that it can identify new target objects that were never learned in the primary model while improving the matching accuracy of what has been learned. We conduct extensive experiments on CAVIAR, ISCAPS databases and our own databases where the camera views are disjoint and the appearance of objects changes significantly due to variations in the camera viewpoint, illumination, weather and poses. The experiments demonstrate that our proposed model is superior to that of existing classification-based recognition methods in terms of accuracy, robustness and computation efficiency. The developed methodology can be used in retrieval, matching and other real-time video surveillance applications.