ACM Computing Surveys (CSUR)
Learning Patterns of Activity Using Real-Time Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Non-parametric Model for Background Subtraction
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
Color image quantization for frame buffer display
SIGGRAPH '82 Proceedings of the 9th annual conference on Computer graphics and interactive techniques
Detection of temporarily static regions by processing video at different frame rates
AVSS '07 Proceedings of the 2007 IEEE Conference on Advanced Video and Signal Based Surveillance
Image change detection algorithms: a systematic survey
IEEE Transactions on Image Processing
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People naturally identify rapidly moving foreground and ignore persistent background. Identifying background pixels belonging to stable, chromatically clustered objects is important for efficient scene processing. This paper presents a technique that exploits this facet of human perception to improve performance and efficiency of background modeling on embedded vision platforms. Previous work on the Multimodal Mean (MMean) approach achieves high quality foreground extraction (comparable to Mixture of Gaussians (MoG)) using fast integer computation and a compact memory representation. This paper introduces a more efficient hybrid technique that combines MMean with palette-based background matching based on the chromatic distribution in the scene. This hybrid technique suppresses computationally expensive model update and adaptation, providing a 45% execution time speedup over MMean. It reduces model storage requirements by 58% over a MMean-only implementation. This background analysis enables higher frame rate, lower cost embedded vision systems.